DARP reparameterizes imitation learning around local neighborhood structure using k-NN expert states, actions, and relative distance vectors, delivering 15-46% gains over behavior cloning in control and manipulation tasks.
Shankar Sastry, Jiajun Wu, Koushil Sreenath, Saurabh Gupta, and Xue Bin Peng
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SeqWM introduces sequential autoregressive agent-wise world models for multi-robot MBRL, outperforming baselines in performance and sample efficiency on Bi-DexHands and Multi-Quadruped tasks with physical robot deployment.
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Difference-Aware Retrieval Policies for Imitation Learning
DARP reparameterizes imitation learning around local neighborhood structure using k-NN expert states, actions, and relative distance vectors, delivering 15-46% gains over behavior cloning in control and manipulation tasks.
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Empowering Multi-Robot Cooperation via Sequential World Models
SeqWM introduces sequential autoregressive agent-wise world models for multi-robot MBRL, outperforming baselines in performance and sample efficiency on Bi-DexHands and Multi-Quadruped tasks with physical robot deployment.