arXiv subjects

Shengbang Liu

Publications and source records attributed to Shengbang Liu.

4 recordsLinked to original sources

arXiv2026

Robo-Dopamine 2.0: History-Conditioned and OOD-Aware Process Reward Modeling for Robotic Manipulation

Vision-language-action (VLA) models improve robotic manipulation but remain vulnerable to compounding errors, scene changes, and off-trajectory states. Reinforcement learning can refine pretrained VLA policies, yet sparse success signals hinder exploration, while engineered dense rewards are costly and task-specific. Existing learned visual reward models often rely on static before-after observations, causing temporal ambiguity and weak discrimination between robustness-preserving variations and task-invalid failures under out-of-distribution (OOD) execution. We introduce Robo-Dopamine 2.0, a history- and OOD-aware process reward model with a pairwise prediction interface. It combines (1) history-conditioned pairwise rewards that use source-aligned reference panels for synthetic OOD queries and observed rollout history for online queries, while preserving the queried endpoints, and (2) an OOD-aware signed progress space that represents valid progress, robustness, failure, and recovery. A Signed-Hop Curriculum with transition-aware replay learns coarse execution ordering before fine-grained progress calibration. We also construct an OOD trajectory dataset and a five-family benchmark. Reference panels improve mean visual order consistency (VOC) from 0.967 to 0.986 and OOD-robust VOC from 0.906 to 0.958. With the same 400K pairwise-reward budget, Signed-Hop training with 25% replay reaches 0.9872 mean VOC, compared with 0.9858 for a matched-pool shuffled control. In downstream reinforcement learning, the full model achieves 86.8% mean RoboTwin success and 71/80 successful real-world insertions.

cs.RO↗

arXiv2026

TACO: TActile World Model as a Self-COrrector forScalable VLA Post-Training

Vision-Language-Action (VLA) models have shown promising generalization in robotic manipulation, but they still struggle with contact-rich tasks, where minor contact perturbations can cause unrecoverable failures that are hard to detect from vision alone. Since these failures are localized rather than task-level semantic errors, tactile-aware corrective post-training offers an efficient way to improve recovery. However, scaling such supervision through human intervention is costly. Recent works have explored world models to synthesize imagined rollouts for policy improvement, but vision-only world models may produce visually plausible yet contact-inconsistent trajectories. We therefore introduce TACO, a tactile-aware world-model-driven framework for scalable VLA post-training in contact-rich manipulation. Given real robot rollouts, TACO follows a Recognize-Imagine-Label loop with a tactile-aware world model: a unified progress-action model recognizes failure-adjacent states using progress estimates, a visuo-tactile generation model imagines local correction segments, and the progress-action model labels them with executable corrective actions. To incorporate tactile corrective supervision into VLA post-training, TACO combines knowledge-insulated tactile adaptation with advantage-conditioned training, enabling the policy to learn from imagined corrections without degrading pretrained visual-language priors. These components enable TACO to convert real-world failures into imagined visuo-tactile corrections for iterative VLA post-training. Experiments on real-world contact-rich manipulation tasks show that TACO achieves 44% absolute success rate improvement over the base policy and 32% over the policy without knowledge-insulated tactile adaptation.

cs.RO↗

arXiv2025

Video2Act: A Dual-System Video Diffusion Policy with Robotic Spatio-Motional Modeling

Robust perception and dynamics modeling are fundamental to real-world robotic policy learning. Recent methods employ video diffusion models (VDMs) to enhance robotic policies, improving their understanding and modeling of the physical world. However, existing approaches overlook the coherent and physically consistent motion representations inherently encoded across frames in VDMs. To this end, we propose Video2Act, a framework that efficiently guides robotic action learning by explicitly integrating spatial and motion-aware representations. Building on the inherent representations of VDMs, we extract foreground boundaries and inter-frame motion variations while filtering out background noise and task-irrelevant biases. These refined representations are then used as additional conditioning inputs to a diffusion transformer (DiT) action head, enabling it to reason about what to manipulate and how to move. To mitigate inference inefficiency, we propose an asynchronous dual-system design, where the VDM functions as the slow System 2 and the DiT head as the fast System 1, working collaboratively to generate adaptive actions. By providing motion-aware conditions to System 1, Video2Act maintains stable manipulation even with low-frequency updates from the VDM. For evaluation, Video2Act surpasses previous state-of-the-art VLA methods by 7.7% in simulation and 21.7% in real-world tasks in terms of average success rate, further exhibiting strong generalization capabilities.

cs.RO↗

arXiv2025

Decomposed Object Manipulation via Dual-Actor Policy

Object manipulation, which focuses on learning to perform tasks on similar parts across different types of objects, can be divided into an approaching stage and a manipulation stage. However, previous works often ignore this characteristic of the task and rely on a single policy to directly learn the whole process of object manipulation. To address this problem, we propose a novel Dual-Actor Policy, termed DAP, which explicitly considers different stages and leverages heterogeneous visual priors to enhance each stage. Specifically, we introduce an affordance-based actor to locate the functional part in the manipulation task, thereby improving the approaching process. Following this, we propose a motion flow-based actor to capture the movement of the component, facilitating the manipulation process. Finally, we introduce a decision maker to determine the current stage of DAP and select the corresponding actor. Moreover, existing object manipulation datasets contain few objects and lack the visual priors needed to support training. To address this, we construct a simulated dataset, the Dual-Prior Object Manipulation Dataset, which combines the two visual priors and includes seven tasks, including two challenging long-term, multi-stage tasks. Experimental results on our dataset, the RoboTwin benchmark and real-world scenarios illustrate that our method consistently outperforms the SOTA method by 5.55%, 14.7% and 10.4% on average respectively.

cs.RO↗