Instruction: Put the tennis ball on the box.

VLM scene describer running...

System prompt

You are a helpful assistant for a legged robot equipped with a single arm and a two-finger gripper. You specialize in generating accurate and concise scene descriptions. You always apply chain-of-thought reasoning to ensure accurate and comprehensive scene understanding.

User prompt

The robot received this instruction from the user: Put the tennis ball on the box. Based on your observation of the image, provide a short scene description that focuses on the spatial relationships between the target object and nearby objects the robot may need to interact with. Describe only the scene, do not include the robot’s task in the description. Here's the robot's current observation.

VLM task planner running...

System prompt

You are a helpful assistant for a legged robot equipped with a single arm and a two-finger gripper. You specialize in task planning, and you always suggest the plan that is most likely to fulfill the task. You learn and adapt from previous experience in the long-term memory and also the current short-term memory (especially the action failures). Always apply chain-of-thought reasoning by thinking step by step before making a final decision.

User prompt

The robot received this instruction from the user: Put the tennis ball on the box. Here's the robot's current observation. Given the current scene and task, choose the next best action. HARD CONSTRAINTS TO APPLY: - PUSH only works on objects directly on the table (nothing can be between the object and the table). PICK and PLACE would also work. - If the objective is to put an object on top of another object, you must use PICK and PLACE. PUSH would not work. - When two objects are next to each other, it's infeasible to directly PUSH the object that is behind the other one. - When the target object is resting on another object (e.g., a cup on a plate), you must use PICK and PLACE instead of PUSH. - If the robot is holding something, it must PLACE that object before attempting to grasp another. - When the target object is tiny or flat, which is hard to grasp, you cannot use PICK. - In the case of action failure, never repeat the same action immediately without first rearranging the scene yourself. This is because the same failure is mostly likely to occur, whether the scene has been reset or not. Instead, consider whether other actions could be taken. - Never give a suggestion on how to better execute a failed action—the robot cannot understand or adapt to such advice. GENERAL RULES: - Could interacting with other objects help? You are allowed to interact with any other objects on the table. - Any object on the table can be used as a tool (all are clean and safe to handle). Be creative! But remember to always PICK up the tool first before using it. - In the reasoning process, first propose a promising action, then check whether it violates any constraints, one by one. If it does, discard it and propose another. Repeat this until you find a promising action that satisfies all constraints. ACTION PARAMETERS: - For the pick up, if the object needs to be grasped at a specific section, you must specify that as well. - For the place action, you need to specify which object to place the target object on (not next to). If the object needs to be placed at a specific section, you must specify that as well. - For the push action, you need to specify the direction to push (left or right). When both push directions work, prefer pushing left if the object is on the left to the gripper; right if on the right. Output your final decision in the specified structured format. A human operator may have reset the scene to its initial state after the failure. Here are the past relevant experiences from long-term memory.

{
  "scenario": "Instruction: remove the box off the table.\nScene: The box was on the table.",
  "experience": "Experience: The robot was tasked with removing the box off the table. It pushed the box off the table, which succeeded as the box disappeared from the tabletop."
}

{
  "scenario": "Instruction: Bring the two cups together, then place the ball in one.\nScene: Two ceramic cups are spaced apart on the table; a ball lies nearby.",
  "experience": "Experience: The robot was tasked with bringing the two cups together and placing a ball in one. It pushed the left cup toward the right, then picked up the ball and placed it in the right cup, which succeeded as both cups were now close together and the ball was inside one cup."
}

{
  "scenario": "Instruction: Push the cereal box to the corner.\nScene: The box was near the center of the kitchen table.",
  "experience": "Experience: The robot was tasked with pushing the cereal box to the corner. It pushed the cereal box to the corner, which succeeded as the box was now positioned in the corner."
}

{
  "scenario": "Instruction: Put the apple on the plate.\nScene: A white plate is on the left side of the table, and an apple is on the right side, near a cylindrical container.",
  "experience": "Experience: The robot was tasked with placing an apple on a plate. Initially, it failed to pick up the apple due to the proximity of a container. After pushing the container away to clear the area, the robot successfully picked up the apple and placed it on the plate. The key lesson is to ensure a clear workspace around the target object before attempting to pick it up, which can prevent action failures due to obstructions."
}

{
  "scenario": "Instruction: Place the can next to the glass.\nScene: The glass is centered on the table.",
  "experience": "Experience: The robot was tasked with placing the can next to the glass. It placed the can to the right of the glass, which succeeded as the can was now standing securely next to the glass."
}

They illustrate how the robot successfully planned action sequences to complete tasks, sometimes after initial failures. They reflect the robot’s capabilities and limitations. Always first carefully reason how the current scenario is similar to these past ones, including how the target object is similar. Identify any lessons learned that could apply to the current task. This is part of the chain-of-thought reasoning and helps you avoid repeating past failures.

VLM success detector running...

System prompt

You are a helpful assistant for a legged robot equipped with a single arm and a two-finger gripper. You specialize in detecting whether an action or task has been successfully completed, and you propose the most likely causes of failure. You always apply chain-of-thought reasoning by thinking step by step and thoroughly analyzing each possible cause. Each action execution involves perception and manipulation. The robot has a limited accuracy in the action execution. You are aware that the robot has limited perception and manipulation capabilities — be realistic but not overly harsh in your success evaluation. This is a robot, not a human! Crucially, any violation of implicit social norms (e.g., knocking over objects, spilling, breaking things) must be treated as a failure, even if not explicitly mentioned in the task.

User prompt

Task given to the robot: Put the tennis ball on the box. Action the robot just attempted: Push the mug slightly to the right to clear space around the tennis ball. Caption: Observation before the action was attempted. Caption: Observation after the action was attempted. This image is captured after the robot completed the action and returned its arm to the default position. Here is the short-term memory for the current task so far for reference:

{
  "time_step": 1,
  "action": {
    "scene_description": "The tennis ball is on the right side of the table, next to a black mug. An open orange box is on the left side of the table.",
    "applicable_knowledge": "In a past scenario, the robot failed to pick up an apple due to obstruction and succeeded after clearing the area. This suggests ensuring a clear workspace around the target object before picking it up.",
    "chain_of_thought_reasoning": "The task is to place the tennis ball on the box. The ball is next to a mug, which might obstruct the pick action. Based on past experience, it's important to clear the area around the target object to avoid failure. The mug is not directly obstructing the ball, but moving it slightly away could ensure a successful pick action. First, push the mug slightly to the right to clear space around the ball, then pick up the ball and place it on the box.",
    "chosen_action": "Push the mug slightly to the right to clear space around the tennis ball.",
    "chosen_skill": "push",
    "target_object": "mug",
    "push_direction": "right"
  }
}

The robot will continue from the above short-term memory. Based on the above image, task and action, and short-term memory, output your structured evaluation. SUCCESS CRITERIA FOR ACTION AND TASK - For picking up objects, if the object is not lying flat on the table at the original location, you might consider it a success. It can be tilted, still touching the table, or appear to be not lifted, and it still might be considered a success. - For pushing an object toward another object, if these objects are getting much closer, you may consider it a success (they do not need to be touching each other or super close). If the push causes the target object to tip over (but not broken), it is still considered as a success. - For placing, if the object is roughly placed on the target object, it might be considered as a success. It does not need to be perfectly placed on the center of the target object. FAILURE CRITERIA FOR ACTION AND TASK - Always a failure if the robot breaks or drops other objects unintentionally, you must consider it a failure for the current action and thus the overall task (regardless of how it performs). - Action failure if the action clearly did not achieve its goal (e.g., gripper missed entirely, object didn’t move at all). REASONING REQUIREMENTS (IF ACTION FAILED) - Describe the scene before and after, especially spatial changes and object states. - There is a success/failure for the action, and a success/failure for the overall task. Don't confuse them.

Task not completed yet, planning next action...

VLM task planner running...

System prompt

You are a helpful assistant for a legged robot equipped with a single arm and a two-finger gripper. You specialize in task planning, and you always suggest the plan that is most likely to fulfill the task. You learn and adapt from previous experience in the long-term memory and also the current short-term memory (especially the action failures). Always apply chain-of-thought reasoning by thinking step by step before making a final decision.

User prompt

The robot received this instruction from the user: Put the tennis ball on the box. Here's the robot's current observation. Here is the action history for the current task so far for reference:

{
  "time_step": 1,
  "action": {
    "scene_description": "The tennis ball is on the right side of the table, next to a black mug. An open orange box is on the left side of the table.",
    "applicable_knowledge": "In a past scenario, the robot failed to pick up an apple due to obstruction and succeeded after clearing the area. This suggests ensuring a clear workspace around the target object before picking it up.",
    "chain_of_thought_reasoning": "The task is to place the tennis ball on the box. The ball is next to a mug, which might obstruct the pick action. Based on past experience, it's important to clear the area around the target object to avoid failure. The mug is not directly obstructing the ball, but moving it slightly away could ensure a successful pick action. First, push the mug slightly to the right to clear space around the ball, then pick up the ball and place it on the box.",
    "chosen_action": "Push the mug slightly to the right to clear space around the tennis ball.",
    "chosen_skill": "push",
    "target_object": "mug",
    "push_direction": "right"
  }
}

{
  "time_step": 1,
  "evaluation": {
    "scene_description": "Before the action, the tennis ball was next to a black mug, with an open orange box on the left side of the table. After the action, the mug has been moved slightly to the right, creating more space around the tennis ball.",
    "is_action_successful": true,
    "is_task_completed": false
  }
}

This contains the actions proposed and executed, and the feedback after the action execution. Learn from the experience (including past and active logs) and adapt if needed. Given the current scene and task, choose the next best action. HARD CONSTRAINTS TO APPLY: - PUSH only works on objects directly on the table (nothing can be between the object and the table). PICK and PLACE would also work. - If the objective is to put an object on top of another object, you must use PICK and PLACE. PUSH would not work. - When two objects are next to each other, it's infeasible to directly PUSH the object that is behind the other one. - When the target object is resting on another object (e.g., a cup on a plate), you must use PICK and PLACE instead of PUSH. - If the robot is holding something, it must PLACE that object before attempting to grasp another. - When the target object is tiny or flat, which is hard to grasp, you cannot use PICK. - In the case of action failure, never repeat the same action immediately without first rearranging the scene yourself. This is because the same failure is mostly likely to occur, whether the scene has been reset or not. Instead, consider whether other actions could be taken. - Never give a suggestion on how to better execute a failed action—the robot cannot understand or adapt to such advice. GENERAL RULES: - Could interacting with other objects help? You are allowed to interact with any other objects on the table. - Any object on the table can be used as a tool (all are clean and safe to handle). Be creative! But remember to always PICK up the tool first before using it. - In the reasoning process, first propose a promising action, then check whether it violates any constraints, one by one. If it does, discard it and propose another. Repeat this until you find a promising action that satisfies all constraints. ACTION PARAMETERS: - For the pick up, if the object needs to be grasped at a specific section, you must specify that as well. - For the place action, you need to specify which object to place the target object on (not next to). If the object needs to be placed at a specific section, you must specify that as well. - For the push action, you need to specify the direction to push (left or right). When both push directions work, prefer pushing left if the object is on the left to the gripper; right if on the right. Output your final decision in the specified structured format. A human operator may have reset the scene to its initial state after the failure. Here are the past relevant experiences from long-term memory.

{
  "scenario": "Instruction: remove the box off the table.\nScene: The box was on the table.",
  "experience": "Experience: The robot was tasked with removing the box off the table. It pushed the box off the table, which succeeded as the box disappeared from the tabletop."
}

{
  "scenario": "Instruction: Bring the two cups together, then place the ball in one.\nScene: Two ceramic cups are spaced apart on the table; a ball lies nearby.",
  "experience": "Experience: The robot was tasked with bringing the two cups together and placing a ball in one. It pushed the left cup toward the right, then picked up the ball and placed it in the right cup, which succeeded as both cups were now close together and the ball was inside one cup."
}

{
  "scenario": "Instruction: Push the cereal box to the corner.\nScene: The box was near the center of the kitchen table.",
  "experience": "Experience: The robot was tasked with pushing the cereal box to the corner. It pushed the cereal box to the corner, which succeeded as the box was now positioned in the corner."
}

{
  "scenario": "Instruction: Put the apple on the plate.\nScene: A white plate is on the left side of the table, and an apple is on the right side, near a cylindrical container.",
  "experience": "Experience: The robot was tasked with placing an apple on a plate. Initially, it failed to pick up the apple due to the proximity of a container. After pushing the container away to clear the area, the robot successfully picked up the apple and placed it on the plate. The key lesson is to ensure a clear workspace around the target object before attempting to pick it up, which can prevent action failures due to obstructions."
}

{
  "scenario": "Instruction: Place the can next to the glass.\nScene: The glass is centered on the table.",
  "experience": "Experience: The robot was tasked with placing the can next to the glass. It placed the can to the right of the glass, which succeeded as the can was now standing securely next to the glass."
}

They illustrate how the robot successfully planned action sequences to complete tasks, sometimes after initial failures. They reflect the robot’s capabilities and limitations. Always first carefully reason how the current scenario is similar to these past ones, including how the target object is similar. Identify any lessons learned that could apply to the current task. This is part of the chain-of-thought reasoning and helps you avoid repeating past failures.

VLM success detector running...

System prompt

You are a helpful assistant for a legged robot equipped with a single arm and a two-finger gripper. You specialize in detecting whether an action or task has been successfully completed, and you propose the most likely causes of failure. You always apply chain-of-thought reasoning by thinking step by step and thoroughly analyzing each possible cause. Each action execution involves perception and manipulation. The robot has a limited accuracy in the action execution. You are aware that the robot has limited perception and manipulation capabilities — be realistic but not overly harsh in your success evaluation. This is a robot, not a human! Crucially, any violation of implicit social norms (e.g., knocking over objects, spilling, breaking things) must be treated as a failure, even if not explicitly mentioned in the task.

User prompt

Task given to the robot: Put the tennis ball on the box. Action the robot just attempted: Pick up the tennis ball. Caption: Observation before the action was attempted. Caption: Observation after the action was attempted. This image is captured after the robot completed the action and returned its arm to the default position. Here is the short-term memory for the current task so far for reference:

{
  "time_step": 1,
  "action": {
    "scene_description": "The tennis ball is on the right side of the table, next to a black mug. An open orange box is on the left side of the table.",
    "applicable_knowledge": "In a past scenario, the robot failed to pick up an apple due to obstruction and succeeded after clearing the area. This suggests ensuring a clear workspace around the target object before picking it up.",
    "chain_of_thought_reasoning": "The task is to place the tennis ball on the box. The ball is next to a mug, which might obstruct the pick action. Based on past experience, it's important to clear the area around the target object to avoid failure. The mug is not directly obstructing the ball, but moving it slightly away could ensure a successful pick action. First, push the mug slightly to the right to clear space around the ball, then pick up the ball and place it on the box.",
    "chosen_action": "Push the mug slightly to the right to clear space around the tennis ball.",
    "chosen_skill": "push",
    "target_object": "mug",
    "push_direction": "right"
  }
}

{
  "time_step": 1,
  "evaluation": {
    "scene_description": "Before the action, the tennis ball was next to a black mug, with an open orange box on the left side of the table. After the action, the mug has been moved slightly to the right, creating more space around the tennis ball.",
    "is_action_successful": true,
    "is_task_completed": false
  }
}

{
  "time_step": 2,
  "action": {
    "scene_description": "The tennis ball is in the center of the table, with an open orange box on the left and a black mug on the right. The mug has been moved slightly to the right, creating space around the tennis ball.",
    "applicable_knowledge": "In a past scenario, the robot successfully placed an apple on a plate after clearing obstructions. This suggests ensuring a clear workspace around the target object before picking it up.",
    "chain_of_thought_reasoning": "The task is to place the tennis ball on the box. The ball is now clear of obstructions after moving the mug. The next step is to pick up the tennis ball and place it on the box. This action aligns with the successful strategy used in the past to place an apple on a plate after clearing obstructions.",
    "chosen_action": "Pick up the tennis ball.",
    "chosen_skill": "pick",
    "target_object": "tennis ball",
    "should_grasp_at_specific_section": false
  }
}

The robot will continue from the above short-term memory. Based on the above image, task and action, and short-term memory, output your structured evaluation. SUCCESS CRITERIA FOR ACTION AND TASK - For picking up objects, if the object is not lying flat on the table at the original location, you might consider it a success. It can be tilted, still touching the table, or appear to be not lifted, and it still might be considered a success. - For pushing an object toward another object, if these objects are getting much closer, you may consider it a success (they do not need to be touching each other or super close). If the push causes the target object to tip over (but not broken), it is still considered as a success. - For placing, if the object is roughly placed on the target object, it might be considered as a success. It does not need to be perfectly placed on the center of the target object. FAILURE CRITERIA FOR ACTION AND TASK - Always a failure if the robot breaks or drops other objects unintentionally, you must consider it a failure for the current action and thus the overall task (regardless of how it performs). - Action failure if the action clearly did not achieve its goal (e.g., gripper missed entirely, object didn’t move at all). REASONING REQUIREMENTS (IF ACTION FAILED) - Describe the scene before and after, especially spatial changes and object states. - There is a success/failure for the action, and a success/failure for the overall task. Don't confuse them.

Task not completed yet, planning next action...

VLM task planner running...

System prompt

You are a helpful assistant for a legged robot equipped with a single arm and a two-finger gripper. You specialize in task planning, and you always suggest the plan that is most likely to fulfill the task. You learn and adapt from previous experience in the long-term memory and also the current short-term memory (especially the action failures). Always apply chain-of-thought reasoning by thinking step by step before making a final decision.

User prompt

The robot received this instruction from the user: Put the tennis ball on the box. Here's the robot's current observation. Here is the action history for the current task so far for reference:

{
  "time_step": 1,
  "action": {
    "scene_description": "The tennis ball is on the right side of the table, next to a black mug. An open orange box is on the left side of the table.",
    "applicable_knowledge": "In a past scenario, the robot failed to pick up an apple due to obstruction and succeeded after clearing the area. This suggests ensuring a clear workspace around the target object before picking it up.",
    "chain_of_thought_reasoning": "The task is to place the tennis ball on the box. The ball is next to a mug, which might obstruct the pick action. Based on past experience, it's important to clear the area around the target object to avoid failure. The mug is not directly obstructing the ball, but moving it slightly away could ensure a successful pick action. First, push the mug slightly to the right to clear space around the ball, then pick up the ball and place it on the box.",
    "chosen_action": "Push the mug slightly to the right to clear space around the tennis ball.",
    "chosen_skill": "push",
    "target_object": "mug",
    "push_direction": "right"
  }
}

{
  "time_step": 1,
  "evaluation": {
    "scene_description": "Before the action, the tennis ball was next to a black mug, with an open orange box on the left side of the table. After the action, the mug has been moved slightly to the right, creating more space around the tennis ball.",
    "is_action_successful": true,
    "is_task_completed": false
  }
}

{
  "time_step": 2,
  "action": {
    "scene_description": "The tennis ball is in the center of the table, with an open orange box on the left and a black mug on the right. The mug has been moved slightly to the right, creating space around the tennis ball.",
    "applicable_knowledge": "In a past scenario, the robot successfully placed an apple on a plate after clearing obstructions. This suggests ensuring a clear workspace around the target object before picking it up.",
    "chain_of_thought_reasoning": "The task is to place the tennis ball on the box. The ball is now clear of obstructions after moving the mug. The next step is to pick up the tennis ball and place it on the box. This action aligns with the successful strategy used in the past to place an apple on a plate after clearing obstructions.",
    "chosen_action": "Pick up the tennis ball.",
    "chosen_skill": "pick",
    "target_object": "tennis ball",
    "should_grasp_at_specific_section": false
  }
}

{
  "time_step": 2,
  "evaluation": {
    "scene_description": "Before the action, the tennis ball was in the center of the table, with an open orange box on the left and a black mug on the right. After the action, the tennis ball is now held by the robot's gripper, indicating it has been successfully picked up from the table.",
    "is_action_successful": true,
    "is_task_completed": false
  }
}

This contains the actions proposed and executed, and the feedback after the action execution. Learn from the experience (including past and active logs) and adapt if needed. Given the current scene and task, choose the next best action. HARD CONSTRAINTS TO APPLY: - PUSH only works on objects directly on the table (nothing can be between the object and the table). PICK and PLACE would also work. - If the objective is to put an object on top of another object, you must use PICK and PLACE. PUSH would not work. - When two objects are next to each other, it's infeasible to directly PUSH the object that is behind the other one. - When the target object is resting on another object (e.g., a cup on a plate), you must use PICK and PLACE instead of PUSH. - If the robot is holding something, it must PLACE that object before attempting to grasp another. - When the target object is tiny or flat, which is hard to grasp, you cannot use PICK. - In the case of action failure, never repeat the same action immediately without first rearranging the scene yourself. This is because the same failure is mostly likely to occur, whether the scene has been reset or not. Instead, consider whether other actions could be taken. - Never give a suggestion on how to better execute a failed action—the robot cannot understand or adapt to such advice. GENERAL RULES: - Could interacting with other objects help? You are allowed to interact with any other objects on the table. - Any object on the table can be used as a tool (all are clean and safe to handle). Be creative! But remember to always PICK up the tool first before using it. - In the reasoning process, first propose a promising action, then check whether it violates any constraints, one by one. If it does, discard it and propose another. Repeat this until you find a promising action that satisfies all constraints. ACTION PARAMETERS: - For the pick up, if the object needs to be grasped at a specific section, you must specify that as well. - For the place action, you need to specify which object to place the target object on (not next to). If the object needs to be placed at a specific section, you must specify that as well. - For the push action, you need to specify the direction to push (left or right). When both push directions work, prefer pushing left if the object is on the left to the gripper; right if on the right. Output your final decision in the specified structured format. A human operator may have reset the scene to its initial state after the failure. Here are the past relevant experiences from long-term memory.

{
  "scenario": "Instruction: remove the box off the table.\nScene: The box was on the table.",
  "experience": "Experience: The robot was tasked with removing the box off the table. It pushed the box off the table, which succeeded as the box disappeared from the tabletop."
}

{
  "scenario": "Instruction: Bring the two cups together, then place the ball in one.\nScene: Two ceramic cups are spaced apart on the table; a ball lies nearby.",
  "experience": "Experience: The robot was tasked with bringing the two cups together and placing a ball in one. It pushed the left cup toward the right, then picked up the ball and placed it in the right cup, which succeeded as both cups were now close together and the ball was inside one cup."
}

{
  "scenario": "Instruction: Push the cereal box to the corner.\nScene: The box was near the center of the kitchen table.",
  "experience": "Experience: The robot was tasked with pushing the cereal box to the corner. It pushed the cereal box to the corner, which succeeded as the box was now positioned in the corner."
}

{
  "scenario": "Instruction: Put the apple on the plate.\nScene: A white plate is on the left side of the table, and an apple is on the right side, near a cylindrical container.",
  "experience": "Experience: The robot was tasked with placing an apple on a plate. Initially, it failed to pick up the apple due to the proximity of a container. After pushing the container away to clear the area, the robot successfully picked up the apple and placed it on the plate. The key lesson is to ensure a clear workspace around the target object before attempting to pick it up, which can prevent action failures due to obstructions."
}

{
  "scenario": "Instruction: Place the can next to the glass.\nScene: The glass is centered on the table.",
  "experience": "Experience: The robot was tasked with placing the can next to the glass. It placed the can to the right of the glass, which succeeded as the can was now standing securely next to the glass."
}

They illustrate how the robot successfully planned action sequences to complete tasks, sometimes after initial failures. They reflect the robot’s capabilities and limitations. Always first carefully reason how the current scenario is similar to these past ones, including how the target object is similar. Identify any lessons learned that could apply to the current task. This is part of the chain-of-thought reasoning and helps you avoid repeating past failures.

VLM success detector running...

System prompt

You are a helpful assistant for a legged robot equipped with a single arm and a two-finger gripper. You specialize in detecting whether an action or task has been successfully completed, and you propose the most likely causes of failure. You always apply chain-of-thought reasoning by thinking step by step and thoroughly analyzing each possible cause. Each action execution involves perception and manipulation. The robot has a limited accuracy in the action execution. You are aware that the robot has limited perception and manipulation capabilities — be realistic but not overly harsh in your success evaluation. This is a robot, not a human! Crucially, any violation of implicit social norms (e.g., knocking over objects, spilling, breaking things) must be treated as a failure, even if not explicitly mentioned in the task.

User prompt

Task given to the robot: Put the tennis ball on the box. Action the robot just attempted: Place the tennis ball on the box. Caption: Observation before the action was attempted. Caption: Observation after the action was attempted. This image is captured after the robot completed the action and returned its arm to the default position. Here is the short-term memory for the current task so far for reference:

{
  "time_step": 1,
  "action": {
    "scene_description": "The tennis ball is on the right side of the table, next to a black mug. An open orange box is on the left side of the table.",
    "applicable_knowledge": "In a past scenario, the robot failed to pick up an apple due to obstruction and succeeded after clearing the area. This suggests ensuring a clear workspace around the target object before picking it up.",
    "chain_of_thought_reasoning": "The task is to place the tennis ball on the box. The ball is next to a mug, which might obstruct the pick action. Based on past experience, it's important to clear the area around the target object to avoid failure. The mug is not directly obstructing the ball, but moving it slightly away could ensure a successful pick action. First, push the mug slightly to the right to clear space around the ball, then pick up the ball and place it on the box.",
    "chosen_action": "Push the mug slightly to the right to clear space around the tennis ball.",
    "chosen_skill": "push",
    "target_object": "mug",
    "push_direction": "right"
  }
}

{
  "time_step": 1,
  "evaluation": {
    "scene_description": "Before the action, the tennis ball was next to a black mug, with an open orange box on the left side of the table. After the action, the mug has been moved slightly to the right, creating more space around the tennis ball.",
    "is_action_successful": true,
    "is_task_completed": false
  }
}

{
  "time_step": 2,
  "action": {
    "scene_description": "The tennis ball is in the center of the table, with an open orange box on the left and a black mug on the right. The mug has been moved slightly to the right, creating space around the tennis ball.",
    "applicable_knowledge": "In a past scenario, the robot successfully placed an apple on a plate after clearing obstructions. This suggests ensuring a clear workspace around the target object before picking it up.",
    "chain_of_thought_reasoning": "The task is to place the tennis ball on the box. The ball is now clear of obstructions after moving the mug. The next step is to pick up the tennis ball and place it on the box. This action aligns with the successful strategy used in the past to place an apple on a plate after clearing obstructions.",
    "chosen_action": "Pick up the tennis ball.",
    "chosen_skill": "pick",
    "target_object": "tennis ball",
    "should_grasp_at_specific_section": false
  }
}

{
  "time_step": 2,
  "evaluation": {
    "scene_description": "Before the action, the tennis ball was in the center of the table, with an open orange box on the left and a black mug on the right. After the action, the tennis ball is now held by the robot's gripper, indicating it has been successfully picked up from the table.",
    "is_action_successful": true,
    "is_task_completed": false
  }
}

{
  "time_step": 3,
  "action": {
    "scene_description": "The tennis ball is held by the robot's gripper. An open orange box is on the left side of the table, and a black mug is on the right side.",
    "applicable_knowledge": "In a past scenario, the robot successfully placed an apple on a plate after clearing obstructions. This suggests ensuring a clear workspace around the target object before picking it up. However, the current task involves placing an object, which aligns with the successful strategy used in the past to place an apple on a plate.",
    "chain_of_thought_reasoning": "The task is to place the tennis ball on the box. The ball is already held by the robot's gripper, and the box is open and accessible on the left side of the table. The past experience of placing an apple on a plate suggests that the robot can successfully place the ball on the box without any obstructions. Since the ball is already picked up, the next logical step is to place it on the box.",
    "chosen_action": "Place the tennis ball on the box.",
    "chosen_skill": "place",
    "target_object": "tennis ball",
    "placement_object": "box",
    "should_place_at_specific_section": false
  }
}

The robot will continue from the above short-term memory. Based on the above image, task and action, and short-term memory, output your structured evaluation. SUCCESS CRITERIA FOR ACTION AND TASK - For picking up objects, if the object is not lying flat on the table at the original location, you might consider it a success. It can be tilted, still touching the table, or appear to be not lifted, and it still might be considered a success. - For pushing an object toward another object, if these objects are getting much closer, you may consider it a success (they do not need to be touching each other or super close). If the push causes the target object to tip over (but not broken), it is still considered as a success. - For placing, if the object is roughly placed on the target object, it might be considered as a success. It does not need to be perfectly placed on the center of the target object. FAILURE CRITERIA FOR ACTION AND TASK - Always a failure if the robot breaks or drops other objects unintentionally, you must consider it a failure for the current action and thus the overall task (regardless of how it performs). - Action failure if the action clearly did not achieve its goal (e.g., gripper missed entirely, object didn’t move at all). REASONING REQUIREMENTS (IF ACTION FAILED) - Describe the scene before and after, especially spatial changes and object states. - There is a success/failure for the action, and a success/failure for the overall task. Don't confuse them.

Task completed. VLM experience summarizer running...

System prompt

You are a helpful assistant for a legged robot equipped with a single arm and a two-finger gripper. You specialize in summarizing the robot’s experiences and extracting generalizable lessons learned. You always apply chain-of-thought reasoning to thoroughly analyze each situation before performing the conversion.

User prompt

Instruction: Put the tennis ball on the box. Scene: A tennis ball is on a table near a black mug, with an open orange box to the left. Please summarize the following step-by-step actions and feedback. Make sure to include every single step the robot took. In addition, highlight the key lesson learned, including similar scenarios the robot may encounter and how it should sequence its actions to complete the task without any failure in the future. Please use only ASCII code.

{
  "time_step": 1,
  "action": {
    "scene_description": "The tennis ball is on the right side of the table, next to a black mug. An open orange box is on the left side of the table.",
    "applicable_knowledge": "In a past scenario, the robot failed to pick up an apple due to obstruction and succeeded after clearing the area. This suggests ensuring a clear workspace around the target object before picking it up.",
    "chain_of_thought_reasoning": "The task is to place the tennis ball on the box. The ball is next to a mug, which might obstruct the pick action. Based on past experience, it's important to clear the area around the target object to avoid failure. The mug is not directly obstructing the ball, but moving it slightly away could ensure a successful pick action. First, push the mug slightly to the right to clear space around the ball, then pick up the ball and place it on the box.",
    "chosen_action": "Push the mug slightly to the right to clear space around the tennis ball.",
    "chosen_skill": "push",
    "target_object": "mug",
    "push_direction": "right"
  }
}

{
  "time_step": 1,
  "evaluation": {
    "scene_description": "Before the action, the tennis ball was next to a black mug, with an open orange box on the left side of the table. After the action, the mug has been moved slightly to the right, creating more space around the tennis ball.",
    "is_action_successful": true,
    "is_task_completed": false
  }
}

{
  "time_step": 2,
  "action": {
    "scene_description": "The tennis ball is in the center of the table, with an open orange box on the left and a black mug on the right. The mug has been moved slightly to the right, creating space around the tennis ball.",
    "applicable_knowledge": "In a past scenario, the robot successfully placed an apple on a plate after clearing obstructions. This suggests ensuring a clear workspace around the target object before picking it up.",
    "chain_of_thought_reasoning": "The task is to place the tennis ball on the box. The ball is now clear of obstructions after moving the mug. The next step is to pick up the tennis ball and place it on the box. This action aligns with the successful strategy used in the past to place an apple on a plate after clearing obstructions.",
    "chosen_action": "Pick up the tennis ball.",
    "chosen_skill": "pick",
    "target_object": "tennis ball",
    "should_grasp_at_specific_section": false
  }
}

{
  "time_step": 2,
  "evaluation": {
    "scene_description": "Before the action, the tennis ball was in the center of the table, with an open orange box on the left and a black mug on the right. After the action, the tennis ball is now held by the robot's gripper, indicating it has been successfully picked up from the table.",
    "is_action_successful": true,
    "is_task_completed": false
  }
}

{
  "time_step": 3,
  "action": {
    "scene_description": "The tennis ball is held by the robot's gripper. An open orange box is on the left side of the table, and a black mug is on the right side.",
    "applicable_knowledge": "In a past scenario, the robot successfully placed an apple on a plate after clearing obstructions. This suggests ensuring a clear workspace around the target object before picking it up. However, the current task involves placing an object, which aligns with the successful strategy used in the past to place an apple on a plate.",
    "chain_of_thought_reasoning": "The task is to place the tennis ball on the box. The ball is already held by the robot's gripper, and the box is open and accessible on the left side of the table. The past experience of placing an apple on a plate suggests that the robot can successfully place the ball on the box without any obstructions. Since the ball is already picked up, the next logical step is to place it on the box.",
    "chosen_action": "Place the tennis ball on the box.",
    "chosen_skill": "place",
    "target_object": "tennis ball",
    "placement_object": "box",
    "should_place_at_specific_section": false
  }
}

{
  "time_step": 3,
  "evaluation": {
    "scene_description": "Before the action, the tennis ball was held by the robot's gripper, with an open orange box on the left side of the table. After the action, the tennis ball is placed inside the open orange box, which is on the left side of the table. The black mug remains on the right side of the table.",
    "is_action_successful": true,
    "is_task_completed": true
  }
}