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In real-world multi-robot systems, performing high-quality, collaborative behaviors requires robots to asynchronously reason about high-level action selection at varying time durations. Macro-Action Decentralized Partially Observable Markov Decision Processes (MacDec-POMDPs) provide a general framework for asynchronous decision making under uncertainty in fully cooperative multi-agent tasks. However, multi-agent deep reinforcement learning methods have only been developed for (synchronous) primitive-action problems. This paper proposes two Deep Q-Network (DQN) based methods for learning decentralized and centralized macro-action-value functions with novel macro-action trajectory replay buffers introduced for each case. Evaluations on benchmark problems and a larger domain demonstrate the advantage of learning with macro-actions over primitive-actions and the scalability of our approaches.
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ChainedDiffuser: Unifying Trajectory Diffusion and Keypose Prediction for Robotic Manipulation
We present ChainedDiffuser, a policy architecture that unifies action keypose prediction and trajectory diffusion generation for learning robot manipulation from demonstrations. Our main innovation is to use a global transformerbased action predictor to predict actions at keyframes, a task that requires multimodal semantic scene understanding, and to use a local trajectory diffuser to predict trajectory segments that connect predicted macro-actions. ChainedDiffuser sets a new record on established manipulation benchmarks, and outperforms both state-of-the-art keypose (macro-action) prediction models that use motion planners for trajectory prediction, and trajectory diffusion policies that do not predict keyframe macro-actions. We conduct experiments in both simulated and realworld environments and demonstrate ChainedDiffuser’s ability to solve a wide range of manipulation tasks involving interactions with diverse objects.
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- Award ID(s):
- 1849287
- PAR ID:
- 10496022
- Publisher / Repository:
- Proceedings of Machine Learning Research
- Date Published:
- Journal Name:
- Conference on Robot Learning/Proceedings of Machine Learning Research
- ISSN:
- 26403948
- Format(s):
- Medium: X
- Location:
- Atlanta, GA, USA
- Sponsoring Org:
- National Science Foundation
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Accepted Manuscript1.0
- Conference Paper:
- The DOI is not currently available.
