Robot policies that remain broadly competent after deployment can degrade when dynamics, contact, geometry, actuation, or sensing differ from source conditions. We study post-hoc adaptation through structured output-space adapters: compact transformations of samples from a frozen base policy. We introduce Warp, a context-conditioned monotone bijective spline that enables nonlinear sample-dependent reshaping while preserving per-dimension ordering; Translation and Affine provide location-only and location--scale references. Across four ManiSkill tasks with controlled deployment shifts, Warp achieves the strongest recovery in a matched evolution-strategy (ES) adapter study, outperforming controls for spline parameterization, location--scale adaptation, and generic nonlinear capacity. Under PPO, the ordering becomes task dependent, showing that richer adapter structure does not automatically translate into better adaptation under a different optimizer. Finally, we test the practical benefit of this approach on physical SO101 peg insertion. Within an interaction-limited RLPD/SAC pipeline, Warp achieves comparable success to a strong stochastic residual while reducing median successful cycle time by approximately 30%. These results identify structured output-space adaptation as a useful design space for adapting frozen robot policies.
Vision-Language Models (VLMs) hold promise for zero-shot video task-progress estimation in robot learning, yet existing frameworks frequently suffer from confirmation bias, hallucinating progress in failed executions due to implicit success assumptions or relative temporal ranking designs. Naive prompting of frontier models collapses into a binary success detector that fails to capture nuanced, non-linear progress and failures. While post-training approaches can mitigate this through explicit training, they demand expensive real-world robotics data and require computationally intensive post-training for every individual base model. To resolve these limitations, we introduce D&A (Describe & Assess): our approach decouples progress estimation into an objective, task-grounded textual description of scene dynamics followed by an assessment phase using explicit rubrics. By compelling the VLM to synthesize an explicit, text-based definition of absolute success before scoring, the framework anchors evaluations to an external, global criterion rather than the video's internal timeline. Empirical results demonstrate that our method calibrates zero-shot estimates and mitigates confirmation bias, providing fine-grained, partial progress metrics that accurately penalize failed executions while correctly interpolating successful runs. Being purely prompt-based, D&A generalizes across tasks, embodiments, and benchmarks---matching or surpassing post-trained reward models---and its reward estimates enable efficient downstream reinforcement learning.
Philip Huang, Fabio Ramos, Jiaoyang Li, Caelan Garrett, Lars Johannsmeier
Multi-arm manipulation has the potential to dramatically improve the throughput, efficiency, and dexterity of many manipulation tasks. Recent work coordinates multiple robot arms in a shared workcell by allocating and scheduling tasks for greater parallelism, jointly planning multi-arm motions, or directly learning multi-arm policies. So far, these approaches are either too slow, rely on domain-specific assumptions, or scale poorly with the number of robots and task horizon. Thus, we introduce cuArms, a conflict-based planner co-designed with GPU parallelism that efficiently decomposes the search space using reusable multi-modal roadmaps for individual robots and resolves conflicts and deadlocks through a lazy search. To jointly optimize task assignment, schedules, motions, and manipulation skills, we integrate a high-level scheduler that proposes candidate skeletons for cuArms to ground into coordinated manipulation plans. Experimental results show that our method establishes a new state of the art, with real-time initial solutions and anytime improvements in both motion quality and scheduling, without days of domain-specific training required by learning approaches. Videos of both simulated and real-world industrial tasks are available on https://cuarms.github.io/.