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SPIRE: Synergistic Planning, Imitation, and Reinforcement Learning for Long-Horizon Manipulation (2410.18065v1)

Published 23 Oct 2024 in cs.RO, cs.AI, cs.CV, and cs.LG

Abstract: Robot learning has proven to be a general and effective technique for programming manipulators. Imitation learning is able to teach robots solely from human demonstrations but is bottlenecked by the capabilities of the demonstrations. Reinforcement learning uses exploration to discover better behaviors; however, the space of possible improvements can be too large to start from scratch. And for both techniques, the learning difficulty increases proportional to the length of the manipulation task. Accounting for this, we propose SPIRE, a system that first uses Task and Motion Planning (TAMP) to decompose tasks into smaller learning subproblems and second combines imitation and reinforcement learning to maximize their strengths. We develop novel strategies to train learning agents when deployed in the context of a planning system. We evaluate SPIRE on a suite of long-horizon and contact-rich robot manipulation problems. We find that SPIRE outperforms prior approaches that integrate imitation learning, reinforcement learning, and planning by 35% to 50% in average task performance, is 6 times more data efficient in the number of human demonstrations needed to train proficient agents, and learns to complete tasks nearly twice as efficiently. View https://sites.google.com/view/spire-corl-2024 for more details.

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Summary

  • The paper introduces a novel SPIRE framework that synergizes task planning, imitation, and reinforcement learning to tackle long-horizon robotic manipulation challenges.
  • It demonstrates that using behavior cloning to warmstart reinforcement learning boosts task success rates from 10% to 94% in complex manipulation scenarios.
  • The framework employs parallelized TAMP scheduling to enhance data efficiency and substantially reduce dependence on extensive human demonstrations.

SPIRE: Synergistic Planning, Imitation, and Reinforcement for Long-Horizon Manipulation

The paper "SPIRE: Synergistic Planning, Imitation, and Reinforcement for Long-Horizon Manipulation" proposes a novel approach to tackle challenges associated with long-horizon, contact-rich robot manipulation tasks. Traditional methods like Imitation Learning (IL) and Reinforcement Learning (RL), although widely applied in robotic manipulation, face limitations when addressing complex tasks. The suggested SPIRE framework synergizes the strengths of Task and Motion Planning (TAMP), IL, and RL to strive beyond these constraints.

Methodology

Task Decomposition and Learning Integration: The SPIRE framework begins by decomposing complex manipulation tasks into manageable subtasks using TAMP. This task decomposition helps in framing subproblems that are easier to learn than the entire task at once. TAMP specifically delineates sections of tasks that an agent can attempt to master, labeled as handoff sections, which are particularly challenging to model explicitly.

Imitation Learning and Bootstrapping:

Initially, SPIRE employs IL to train agents based on human demonstrations within these handoff sections. This paradigmatic approach uses Behavior Cloning (BC) to construct a baseline policy, significantly reducing the burden associated with reward engineering in RL. The training's scaffolding ensures that even imperfect demonstrations suffice to inform preliminary policy structures, addressing the sparsity and complexity inherent in human operational data.

Reinforcement Learning Fine-Tuning:

After generating a BC policy, SPIRE moves towards RL-based fine-tuning, intending to enhance the BC-trained policy's performance further. Key innovations here include warmstarting the RL process using the trained BC policy and employing a Kullback-Leibler (KL) divergence constraint to maintain the RL policy's proximity to the BC policy. These methods address the well-known issue of exploration inefficiency that plagues RL, especially in high-dimensional, sparse-reward environments typical of manipulation tasks.

Parallelized Training with Multi-Worker Scheduling:

To mitigate the typically slow sample collection in RL (exacerbated by the extensive computation times TAMP requires), SPIRE introduces a multi-worker TAMP scheduling framework. By running multiple TAMP workers in parallel, each managing separate sections, SPIRE can efficiently collect environment interactions at a higher throughput than traditional, single-threaded methods.

Experimental Results

The research evaluates SPIRE on a suite of nine complex manipulation tasks. Comparative results show that SPIRE outperforms current state-of-the-art hybrid methods, achieving higher success rates and greater efficiency in execution time. Most notably, in tasks like Tool Hang, SPIRE significantly improves task success rates from 10% using BC alone to 94% after RL fine-tuning, showcasing its potential in enhancing back-boned imitation policies where prior methods falter.

Moreover, it was observed that SPIRE required markedly fewer human demonstrations to achieve high proficiency, showcasing substantial gains in data efficiency. Reduction in data dependency, from 870 demonstrations to just 150 for several tasks, indicates a notable shift towards more practical and scalable robotic training approaches.

Theoretical and Practical Implications

Theoretically, SPIRE provides a well-rounded framework for integrating discrete planning with learned, sequential manipulation tasks. This framework processes these tasks in a manner that capitalizes on IL's visual representation strengths and RL's robust optimization capability. Practically, SPIRE makes strides toward operational feasibility in robotics by reducing both computational and data demands, which are critical bottlenecks in deploying robotics across more fluid real-world scenarios.

Future Directions

Looking forward, SPIRE's architecture could be extended by incorporating adaptive weighting strategies for the KL penalty term, potentially broadening deployment scope through more flexible, situationally reactive policy refinement. Moreover, integrating sequential policy learning with dynamic task environment adaptations may further enhance its robustness and adaptability, aligning with evolving real-world applications such as collaborative multi-agent systems and autonomous robotics.

Overall, SPIRE represents a significant advancement in robot manipulation, leveraging the underlying synergies between planning, imitation, and reinforcement to streamline and enhance learning in complex, uncertain environments.

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