arXiv subjects

Owen Kwon

Publications and source records attributed to Owen Kwon.

3 recordsLinked to original sources

arXiv2026

Training-Free Action Correction for VLA Model Failures via Language Feedback

Vision-Language-Action (VLA) models demonstrate strong semantic understanding yet exhibit systematic failures during deployment. The conditions under which these failures occur, and whether they can be corrected without retraining, remain poorly understood. In this paper, we take steps toward addressing this gap. We present CorrectVLA, a framework that translates task-level natural language corrections into additive action magnitude adjustments without modifying policy weights. A human provides a single task-level correction, applied uniformly across all rollouts without per-episode intervention. In simulation, CorrectVLA recovers execution misalignment failures across both in-distribution and OOD tasks. In real-robot experiments on a UFactory xArm7 under environment shift, CorrectVLA restores near-perfect success where the base policy almost entirely breaks down, generalizing across object locations and identities. Through a taxonomy of failure modes on LIBERO-90, we find that execution misalignment failures, where the policy reaches the correct target but miscalibrates action magnitudes, represent the correctable subset, while other failure modes where semantic comprehension itself breaks down are not amenable to this approach. The approach succeeds when policies possess strategic correctness and fails when fundamental comprehension is absent, establishing a practical operational boundary for inference-time correction.

cs.RO↗

arXiv2026

RIO: Flexible Real-Time Robot I/O for Cross-Embodiment Robot Learning

Despite recent efforts to collect multi-task, multi-embodiment datasets, to design recipes for training Vision-Language-Action models (VLAs), and to showcase these models on different robot platforms, generalist cross-embodiment robot capabilities remains a largely elusive ideal. Progress is limited by fragmented infrastructure: most robot code is highly specific to the exact setup the user decided on, which adds major overhead when attempting to reuse, recycle, or share artifacts between users. We present RIO (Robot I/O), an open source Python framework that provides flexible, lightweight components for robot control, teleoperation, data formatting, sensor configuration, and policy deployment across diverse hardware platforms and morphologies. RIO provides abstractions that enable users to make any choice and to switch between them, with minimal reconfiguration effort. We validate RIO on VLA deployment workflows across three morphologies (single-arm, bimanual, humanoid) and four hardware platforms with varying grippers and cameras. Using teleoperated data collected with RIO, we fine-tune state-of-the-art VLAs including $\pi_{0.5}$ and GR00T on household tasks such as pick-and-place, folding, and bowl scrubbing. By open sourcing all our efforts, we hope the community can accelerate their pace of robot learning on real-world robot hardware. Additional details at: https://robot-i-o.github.io

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arXiv2025

RT-Cache: Training-Free Retrieval for Real-Time Manipulation

Real robots are expected to repeat the same behavior in new environments with very little new data, yet modern controllers either incur heavy per-step inference or require deployment-time fine-tuning. We propose RT-Cache, a training-free retrieval-as-control pipeline that caches diverse image action trajectories in a unified vector memory and, at test time, embeds the current frame to retrieve and replay multi-step snippets, replacing per-step model calls. A hierarchical search keeps lookups sub-second at million scale, shifting cost from compute to storage and enabling real-time control on modest GPUs. Across real-robot tasks and large open logs, RT-Cache achieves higher success and lower completion time than strong retrieval baselines (approximately x2 higher success and ~30% faster in our settings), and a single-episode anchoring study shows immediate adaptation to a more complex, contact-rich task without fine-tuning. RT-Cache turns experience into an append-only memory, offering a simple, scalable path to few-shot deployment today and a foundation for multimodal keys and optional integration with high-level policies. Project page: https://rt-cache.github.io/.

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