RL policy learns robust throwing across variable obstacle layouts
Researchers have extended reinforcement learning for robotic manipulation into cluttered real-world settings by developing a compact potential field representation that lets policies generalize across variable obstacle configurations. Rather than retraining for each new environment, the approach encodes basket targets and obstacle repulsion on fixed grids, enabling a single learned policy to handle arbitrary obstacle layouts. Initialized from kinesthetic demonstrations and refined in simulation, this work bridges a critical gap between lab-controlled throwing tasks and practical deployment where clutter is unavoidable. The technique signals broader progress in making RL-based robot control robust to environmental variation without exponential retraining costs.
Modelwire context
ExplainerThe key insight is not just that the policy generalizes, but that it does so without retraining by encoding environmental variation as a compact learned representation rather than as discrete environment instances. This sidesteps the combinatorial explosion of obstacle configurations that would otherwise require exponential data collection.
This work shares the core problem with the human-in-the-loop meta-learning paper from early July: how to achieve robustness to unseen target conditions without collecting domain-specific data at deployment time. Where that work used expert guidance to shape synthetic data distributions, this approach uses a fixed potential field grid to let a single policy adapt to arbitrary layouts. Both are practical responses to the gap between lab performance and real-world scarcity. The connection to the Valdi world models paper is looser but relevant: both grapple with whether learned representations can compress environmental complexity enough to make real-time control tractable.
If the same policy trained on this potential field representation successfully handles obstacle densities or geometry types not present in the training simulation (e.g., narrow gaps, dynamic obstacles), that confirms the representation is truly generalizing rather than memorizing obstacle patterns. If subsequent work shows the approach fails on out-of-distribution clutter, the claim about arbitrary layouts collapses.
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MentionsTossingBot · reinforcement learning · potential field representation
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Modelwire summarizes, we don’t republish. arXiv cs.LG originally reported this story as “Learning to Throw Objects Safely in Multi-Obstacle Environments”. The full content lives on arxiv.org. If you’re a publisher and want a different summarization policy for your work, see our takedown page.