arXiv subjects

Fred Shentu

Publications and source records attributed to Fred Shentu.

3 recordsLinked to original sources

arXiv2026

WARP-RM: A Warp-Augmented Relative Progress Reward Model for Data Curation

Scaling imitation learning requires large datasets, yet human teleoperation inevitably produces mixed-quality demonstrations containing hesitations, retries, and pauses. Prior frame-level progress reward models supervise on absolute temporal progress proxies that suffer from label noise, or require costly human annotations to define subtask boundaries. We present WARP (Warp-Augmented Relative Progress), a novel fully self-supervised algorithm for learning dense, signed relative progress magnitudes directly from successful demonstrations. WARP generates per-frame progress targets via time-warp augmentations of demonstrations (variable playback speeds and reversals) and we train WARP-RM to predict normalized signed temporal displacement from the start of each sampled window. Aggregating these predictions across overlapping windows yields a dense frame-level progress signal. We then introduce WARP-BC, which uses these scalar reward estimates to filter and reweight action chunks during behavior cloning. We evaluate our approach on a physical bimanual robot system performing a long-horizon deformable object manipulation task: folding T-shirts from a random crumpled start. To evaluate policy robustness against suboptimal data, we construct training datasets of varying quality using episode length as a proxy for teleoperation sub-optimality. Across these training tiers, WARP-BC improves successful-folding throughput by up to ~18x over vanilla BC. Furthermore, we evaluate bottle-in-bin placement in the real world and in simulation. Across 512 paired simulated scenes, WARP-BC achieves 290 bottles/hr versus 237 for vanilla BC and 271 for DemInf, with all curation methods retaining 31.5% of the data. We release open simulation data, code, checkpoints, and evaluation artifacts for end-to-end reproduction of the WARP pipeline. Project page: https://uynitsuj.github.io/warp-rm/

cs.RO↗

arXiv2026

Scalable Behavior Cloning with Open Data, Training, and Evaluation

We introduce ABC, a fully open-source stack for manipulation with behavior cloning. At its core is ABC-130K: the largest open-source teleoperation dataset to date, featuring 3,500 hours of data spanning over 130K episodes across 195 diverse tasks. Furthermore, we open-source our accessible hardware setup, training infrastructure, and simulation pipeline. We also release 400 hours of sim-teleop data and provide a co-training recipe that produces correlated simulation and real-world evaluation, offering a reliable proxy for ablating model-design and training decisions before costly real-world evaluation. We explore various training recipes and compare common architectural choices for Diffusion Transformers (DiT) and Vision-Language-Action (VLA) models, grounding our findings in real-world evaluations. The resulting policies successfully execute dexterous tasks such as box folding and extracting credit cards from wallets. By providing a reproducible toolkit, we aim to place researchers on an equal footing, establishing the necessary foundation to learn the ABCs of Behavior Cloning together as a community.

cs.RO↗

arXiv2017

Probabilistically Safe Policy Transfer

Although learning-based methods have great potential for robotics, one concern is that a robot that updates its parameters might cause large amounts of damage before it learns the optimal policy. We formalize the idea of safe learning in a probabilistic sense by defining an optimization problem: we desire to maximize the expected return while keeping the expected damage below a given safety limit. We study this optimization for the case of a robot manipulator with safety-based torque limits. We would like to ensure that the damage constraint is maintained at every step of the optimization and not just at convergence. To achieve this aim, we introduce a novel method which predicts how modifying the torque limit, as well as how updating the policy parameters, might affect the robot's safety. We show through a number of experiments that our approach allows the robot to improve its performance while ensuring that the expected damage constraint is not violated during the learning process.

cs.RO↗