Generalist robot policies improved via reinforcement learning in multi-task settings
Original title: Reinforcement Learning Enables Scaling Generalist Robot Policy Improvement
Generalist policies enable online multi-task improvement with generalization to unseen tasks.
Generalist robot policies can be improved online across diverse tasks simultaneously using reinforcement learning. The right recipe for value estimation, policy extraction and resource allocation enables generalization. Training task distribution and value-based RL prove critical for this.
- Online multi-task improvement possible for generalist robot policies
- Value estimation and policy extraction are key design choices
- Generalization occurs when training distribution matches deployment
- First evidence for online generalist improvement in robot foundation models
From the original article
1 min read at arxiv.orgThe introduction of generalist robot policies has enabled a single, large model to learn to perform a variety of tasks from a vast pre-training corpus. While these models often exhibit powerful zero-shot generalization capabilities, they typically require an adaptation phase to achieve sufficient performance in deployment.
By Michal Korniak · arxiv.org
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