Research Interests

Learning-Based Control

Projects

Publications

2026

[C119] Inference-Time Robot Behavior Steering through Physically-Aware Reconfiguration of Task-Structure
Yiyuan Pan, Hanjiang Hu, Shangtao Li, Xusheng Luo and Changliu Liu
Conference on Robot Learning, 2026
Citation Formats:
```
    
```
Abstract:

A central challenge in deploying learned robot policies is inference-time behavior steering: redirecting a policy at test time to satisfy user preferences not anticipated during training, without retraining. Existing methods fail in two modes: end-to-end methods require fine-tuning or expert-level guidance, while neuro-symbolic methods rely on predefined symbols whose edits can result in logically reasonable but physically infeasible plans. To address this challenge, we propose ReStruct, which builds upon a neural automaton policy that decomposes a visuomotor policy into a high-level state-machine skeleton capturing task structure and a low-level continuous controller represented as a residual policy. Specifically, ReStruct adopts the automaton to represent the preference and incorporates it into the skeleton through a synchronous product, thereby reconfiguring the task structure. With the controller kept frozen, the action priors provided by the skeleton are updated accordingly to enable physically-aware control under a modified task structure. Extensive experiments from simulation and real-world show that ReStruct steers a wide range of preferences, from object-centric specifications to temporal-logic constraints, and after steering surpasses existing methods, exceeding VLA models in both task success and preference-following by up to 25%.
[C118] λ-Reachability: Geometric-Horizon Safety Bellman Equations for Humanoid Safety
Rui Chen, Shangtao Li, Yifan Sun and Changliu Liu
Conference on Robot Learning, 2026
Citation Formats:
```
    
```
Abstract:

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.

Video:
[C115] Learning Safe-Stoppability Monitors for Humanoid Robots
Yifan Sun, Yiyuan Pan, Shangtao Li, Caiwu Ding, Tao Cui, Lingyun Wang and Changliu Liu
IEEE/RSJ International Conference on Intelligent Robots and Systems, 2026
Citation Formats:
```
    
```
Abstract:

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.

Video:

[W] RoboFin3D: A Sim-to-Real Platform for Robotic Surface Finishing
Haowei Wen, Shangtao Li, Vaibhav Sanjay, Philip Huang, Jiaoyang Li and Changliu Liu
IROS 2026 Workshop on Sim2Real and Classical Control: From Rigorous Theory to Data-Driven Robotics, 2026
Citation Formats:
```
    
```
Abstract:

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%.

[U] ManiSkillFormer: Demonstration-Free Compositional Manipulation via Task-Conditioned Geometric Contracts
Peiqi Yu, Mosam Dabhi, Shangtao Li, Bowei Li, Laszlo Jeni and Changliu Liu
arXiv:2609.16331, 2026
Citation Formats:
```
    
```
Abstract:

Adapting robotic manipulation to new objects and tasks often requires additional demonstrations, policy fine-tuning, or manual engineering. Reusable manipulation skills can reduce this effort, but connecting their execution requirements to scene-specific geometry 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.

Video: