arXiv subjects

Jiaju He

Publications and source records attributed to Jiaju He.

3 recordsLinked to original sources

arXiv2026

AWLS: Advantage-Weighted Latent Steering for One-Step Generative Robotic Manipulation Policies

Human-collected real-world robotic manipulation demonstrations often contain mixed-quality trajectories, including hesitant, indirect, or inefficient action segments. While diffusion- and flow-based policies can model such multimodal data, standard behavior cloning (BC) assigns equal likelihood to all demonstrated actions and may therefore reproduce suboptimal modes. Offline RL value estimates offer a natural signal for biasing policies toward higher-value behavioral modes, but directly fine-tuning the generative policy or using critic gradients for policy steering can amplify critic errors and induce prior drift. We propose Advantage-Weighted Latent Steering (AWLS), an offline post-training method for improving frozen one-step generative manipulation policies without updating their decoders. AWLS learns a residual noise shaper, an observation-conditioned residual correction to the policy's latent noise, and trains it with an advantage-weighted action reconstruction objective. The advantages are detached, scale-normalized, and used only as supervised weights on demonstrated actions, avoiding critic-gradient policy updates while preserving fast single-step inference. We further introduce a rollout-free decoder-sensitivity criterion for selecting pretrained checkpoints that remain responsive to latent-noise perturbations. Experiments on simulated and real-world robotic manipulation tasks with clean and mixed-quality demonstrations show that AWLS improves over the frozen base policy while limiting drift from the pretrained behavior prior. Project page: https://awls-corl2026.github.io/

cs.RO↗

arXiv2026

FR-VLA: Implicit Force Reasoning VLA for Contact-Rich Manipulation

Integrating force awareness into Vision-Language-Action (VLA) models involves not merely encoding wrench measurements as an additional modality, but determining when and how strongly physical interaction should influence robot action generation. Existing VLAs that use force typically inject it through a fixed pathway, without explicitly determining when it is relevant to the current action. This calls for an intermediate force relevance mechanism inside the VLA, in which visual-language context estimates the relevance of physical feedback to the current task before force enters the action pathway. FR-VLA addresses this gap with a Vision-Language Force-Reasoning Model that uses learnable force query tokens to derive a force admission signal grounded in visual-language context, and uses this signal to modulate an external tokenized force pathway for action generation. In real-world experiments on five contact-rich tasks across two robotic platforms, FR-VLA achieves the highest mean success rate over six evaluated stages, and trajectory visualizations show that its force admission varies with the interaction phase.

cs.RO↗

arXiv2026

Noise-Invariant Spatial Encoding for 3D Manipulation Policies under Depth Corruption

3D manipulation policies derive actions from explicit scene geometry rather than color images alone, enabling them to reason over metric distances, object relations, and robot-scene reachability. This geometric interface is also a vulnerability: commodity RGB-D sensors return missing measurements, boundary artifacts, and plausible but wrong surfaces around transparent, reflective, thin, or textureless objects. We study this failure under realistic depth corruption and observe a sharp depth-noise cliff across modern 3D policies, even when they are trained with matched noisy depth. This residual gap motivates a spatial-encoding approach: corrupted depth becomes entangled with the token coordinates and geometry features consumed by the action expert. We propose Noise-Invariant Spatial Encoding (NISE), a 3D policy architecture that learns trustworthy geometry features even facing imperfect depth. NISE uses Geometric Cross-View Self-Supervision to shape shared policy features with calibrated multi-view depth, and Gaussian Marginalized 3D Attention to carry position uncertainty into deployed attention rather than treating every lifted token as exact. Across simulated benchmarks with sensor-realistic corruption and real-robot tasks involving transparent and reflective materials, NISE improves depth-noise robustness while preserving clean-depth performance, supporting 3D policies designed for commodity RGB-D deployment rather than clean-depth benchmarks alone.

cs.RO↗