Visual Room Rearrangement Planning Under Partial Observability
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Solution Overview
Problem
Existing robotic systems face challenges in efficiently planning room rearrangement tasks under partial observability, characterized by uncertainty in object locations, scalability issues with large numbers of objects, combinatorial expansion, and inefficient traversal, particularly in scenarios with blocked goals and swap cases.
Innovation Solution
A method and system utilizing a large language model-based search network, graph representation, and Parameterized Deep-Q Network (P-DQN) with hybrid action space to plan efficient object search and rearrangement, incorporating proxy rewards and graph embeddings to minimize agent traversal and resolve blocked/swap cases.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing methods explore the room to find objects outside the agent's ego-view, then complete object detection is improved, but traversal cost increases significantly
Solution Approach 1:
The system performs preliminary action by using a language model to predict the locations of unseen objects before the agent physically traverses to find them. The language model processes textual descriptions of the room and generates predictions about where objects located outside the current ego-view might be, allowing the agent to plan its traversal more efficiently rather than performing exhaustive exploration.
2Ease of manufacture
If existing methods use non-optimal planning without optimizing the sequence of actions, then implementation simplicity is improved, but the number of steps and traversal required increases
Solution Approach 1:
The system implements feedback by using a reinforcement learning component that learns from the outcomes of previous actions. The agent receives rewards or penalties based on whether its predicted object locations are correct and whether its rearrangement actions successfully achieve the goal state. This feedback loop allows the system to optimize its planning strategy over time, improving rearrangement efficiency while maintaining reasonable implementation complexity.
3Measurement precision
If the system assumes complete visibility of the room through bird's eye view, then planning accuracy is improved, but scalability to large numbers of objects deteriorates
Solution Approach 1:
The system applies segmentation by dividing the room into multiple ego-view segments that the agent can observe from different locations. Instead of requiring complete bird's eye view visibility, the language model processes textual descriptions segmented by location and generates predictions for objects in each segment. This segmentation approach maintains planning accuracy for visible objects while improving scalability to rooms with large numbers of objects distributed across multiple viewable areas.
4Device complexity
If existing methods use greedy planning without considering blocked goals or swap cases, then computational complexity is reduced, but task completion reliability deteriorates
Solution Approach 1:
The system performs preliminary analysis using the language model to identify potential blocked goals and swap cases before executing the rearrangement plan. By processing textual descriptions of the room layout and object relationships, the language model can predict scenarios where objects block each other's goal positions or where swap operations are needed, allowing the agent to plan alternative actions in advance rather than getting stuck during execution.
Data Source
AI summary
A method and system for task planning for visual room rearrangement under partial observability is disclosed. The system or the robotic agent utilizes a visual input to efficiently plan a sequence of actions for simultaneous object search and rearrangement in an untidy room, to achieve a desired tidy state. Unlike search networks in the art that follow ad hoc approach, the method discloses a search network utilizing commonsense knowledge from large language models to find unseen objects. A Deep RL network used for task planning is trained with proxy reward, along with unique graph-based state representation to produce a scalable and effective planner that interleaves object search and rearrangement to minimize the number of steps taken and overall traversal of the agent, and to resolve blocked goal and swap cases. Sample efficient cluster-biased sampling is utilized for simultaneous training of the proxy reward network along with the Deep RL network.