arXiv subjects

Shangtao Li

Publications and source records attributed to Shangtao Li.

6 recordsLinked to original sources

arXiv2026

Inference-Time Robot Behavior Steering through Physically-Aware Reconfiguration of Task-Structure

A central challenge in deploying robot policies is inference-time policy steering: redirecting a policy to satisfy natural-language preferences unseen in training, without retraining. Existing methods fail in two modes: end-to-end policies such as vision-language-action models (VLAs) require fine-tuning or expert-level guidance, while neuro-symbolic methods rely on predefined symbols whose edits can yield logically valid but physically infeasible plans. We propose ReStruct, a zero-shot method built on a neural automaton policy that decomposes a visuomotor policy into a high-level state-machine skeleton and a low-level residual controller. A vision-language model (VLM) grounds the preference in the skeleton's learned symbols, refining them if needed, and compiles it into a temporal-logic automaton, whose synchronous product with the skeleton reconfigures the task structure. With the controller frozen, the skeleton's action priors are updated for physically-aware control. In simulation and real-world experiments, ReStruct steers preferences from object-centric specifications to temporal-logic constraints, including novel combinations of demonstrated behaviors that require compositional generalization. Average preference-following success is 70.9% in simulation and 92.5% on a real robot, surpassing baselines like the prompt-steered vision-language-action model π0.5.

cs.RO↗

arXiv2026

Not an Accident: Exploiting Safety Filter Inadequacy in Humanoid Robots

As humanoid robots move from research laboratories toward deployment in human-centered environments such as homes, hospitals, and warehouses, safe operation around nearby people and objects becomes a fundamental requirement. Safety filters have emerged as a promising technique to address this challenge by providing mathematical guarantees that generated control actions do not result in collisions. This paper shows that these guarantees hold only when the safety parameters are configured correctly. However, correct parameter configurations are task- and environment-specific, and no general method exists to determine them across diverse deployments. As a result, safety filters shipped with humanoid robots can fail after deployment in specific scenarios. We show that attackers can exploit these failures through only normal user interactions without any additional privileges or capabilities. To demonstrate this risk, we present goal insertion attacks and develop SIREN, an automated tool that identifies vulnerable inserting goals for a given task and environment. Experimental results show that SIREN identifies attacks in all vulnerable scenarios in the SPARK dataset. Compared with a baseline approach, SIREN discovers three times as many attacks. We also show that SIREN is able to generate attacks against a real humanoid and cause it to collide while executing tasks. These results demonstrate that current safety filters introduce exploitable vulnerabilities that can cause safety violations, which reveals that existing safety mechanisms can themselves become a source of safety risk for humanoid robots.

cs.CR↗

arXiv2026

RoboFin3D: A Sim-to-Real Platform for Robotic Surface Finishing

Grinding and sanding are fundamental processes in industrial robotic surface finishing. However, physical trials are expensive and consume workpieces, making reproducible experiments difficult. We present RoboFin3D, a sim-to-real platform built on Isaac Sim and the Newton physics engine, that provides physics-based grinding and sanding simulation for cheap and repeatable robotic surface finishing experiments. RoboFin3D utilizes a signed distance field (SDF) to model the changing geometry of the workpiece, enabling contact computation, live updates and rendering without an intermediate mesh. It additionally uses a separate surface field to model progressive surface appearance change during sanding. We also introduce WeldGen, a weld sampling module, to generate weld beads on 8,918 real-world workpiece meshes for providing diverse simulation assets. The simulation parameters are calibrated on real experimental results and our evaluation demonstrates our simulation's fidelity against the real world. We also demonstrate that simulation-generated data can be used to improve the performance of perception models. Simulation-only fine-tuning of SAM2 improves IoU for segmentation of unsanded regions from 77.15% to 84.47%, while combined synthetic and real training reaches 97.41%.

cs.RO↗

arXiv2026

ManiSkillFormer: Demonstration-Free Compositional Manipulation via Geometric Contracts and Agentic Skill Graph

Adapting robotic manipulation to new objects and tasks often requires additional demonstrations or manual engineering. Reusable manipulation skills can reduce this effort, but adapting these skills to new scenes remains challenging. We present ManiSkillFormer, a framework for demonstration-free and compositional manipulation that connects perception and action through explicit geometric contracts. Building on reusable skill schemas, LLM agents generate contracts specifying the geometry primitives required by each skill, together with corresponding motion templates for semantic objects and task contexts. These contracts guide a perception module to ground task-relevant 3D geometry from observations, which is then used to instantiate reusable motion templates in a skill library. We evaluate ManiSkillFormer on a dual-arm robot across demonstration-free pick-and-place with 30 instances from 8 object categories, functional manipulation including unscrewing, pouring, pressing, and folding, and three long-horizon tasks. ManiSkillFormer achieves an average success rate of 88.97% for pick-and-place, 75.00% for functional manipulation, and completion rates of 50--80% across the long-horizon tasks, outperforming the evaluated baselines and two ablated pipelines. These results demonstrate the potential of explicit geometric contracts to support skill reuse and composition across objects and tasks without per-object policy fine-tuning or additional robot demonstrations. The project website is at https://patricia1019.github.io/ManiSkillFormer/.

cs.RO↗

arXiv2026

$λ$-Reachability: Geometric-Horizon Safety Bellman Equations for Humanoid Safety

We introduce $λ$-Reachability, a scalable approach to Hamilton--Jacobi safety analysis for high-dimensional robotic systems. Unlike prior discounted formulations that rely on fixed one-step Bellman updates, $λ$-Reachability employs a stochastic multi-step estimator of the safety value, using a geometrically distributed rollout horizon together with a randomly absorbed terminal. Conceptually analogous to TD($λ$), $λ$-Reachability interpolates between local self-consistency updates and long-horizon max-over-trajectory safety targets via an interpretable horizon-control parameter. Unlike TD($λ$), where the terminal value is always incorporated in learning targets, the terminal safety value in $λ$-Reachability is only used at a probability controlled by parameter $δ$. We formally show that for $δ<1$, the update induces a contraction mapping that allows temporal-difference learning; as $λ\to 1$, the estimator recovers the undiscounted reachability objective. We apply $λ$-Reachability to high-dimensional safety learning problems with both simulated and real humanoid robots under balance and collision avoidance constraints. Experimental results demonstrate that $λ$-Reachability significantly improves both safe-set boundary classification and safety margin estimation compared to single-step temporal-difference baselines.

cs.RO↗

arXiv2026

Learning Safe-Stoppability Monitors for Humanoid Robots

Emergency stop (E-stop) mechanisms are the de facto standard for robot safety. However, for humanoid robots, abruptly cutting power can itself cause catastrophic failures; instead, an emergency stop must execute a predefined fallback controller that preserves balance and drives the robot toward a minimum-risk condition. This raises a critical question: from which states can a humanoid robot safely execute such a stop? In this work, we formalize emergency stopping for humanoids as a policy-dependent safe-stoppability problem and use data-driven approaches to characterize the safe-stoppable envelope. We introduce PRISM (Proactive Refinement of Importance-sampled Stoppability Monitor), a simulation-driven framework that learns a neural predictor for state-level stoppability. PRISM iteratively refines the decision boundary using importance sampling, enabling targeted exploration of rare but safety-critical states. This targeted exploration significantly improves data efficiency while reducing false-safe predictions under a fixed simulation budget. We further demonstrate sim-to-real transfer by deploying the pretrained monitor on a real humanoid platform. Results show that modeling safety as policy-dependent stoppability enables proactive safety monitoring and supports scalable certification of fail-safe behaviors for humanoid robots.

cs.RO↗