arXiv subjects

Philip Huang

Publications and source records attributed to Philip Huang.

8 recordsLinked to original sources

arXiv2026

VAMP-MR: Vector-Accelerated Motion Planning and Execution for Multi-Robot-Arms

Multi-robot-arm motion planning is a key challenge in deploying multiple manipulators for industrial tasks such as manufacturing. Existing search-based and sampling-based solvers often require significant computation time to produce collision-free, high-quality motions suitable for safe real-world execution. In this work, we introduce a new suite of multi-robot-arm motion planners capable of near real-time motion generation, combining classical planning algorithms with state-of-the-art vectorized collision-checking techniques. Based on CPU SIMD instructions, our new planners accelerate their primary bottleneck, collision checking, and achieve up to two orders of magnitude speedup in both motion planning and execution postprocessing for multi-arm manipulation tasks. We also release our implementation to lower the barrier for research and development of multi-robot-arm planning and manipulation problems. Code is available at https://vamp-mr.github.io/vamp-mr

cs.RO↗

arXiv2026

Autonomous Integration and Improvement of Robotic Assembly using Skill Graph Representations

Robotic assembly systems traditionally require substantial manual engineering effort to integrate new tasks, adapt to new environments, and improve performance over time. This paper presents a framework for autonomous integration and continuous improvement of robotic assembly systems based on Skill Graph representations. A Skill Graph organizes robot capabilities as verb-based skills, explicitly linking semantic descriptions (verbs and nouns) with executable policies, pre-conditions, post-conditions, and evaluators. We show how Skill Graphs enable rapid system integration by supporting semantic-level planning over skills, while simultaneously grounding execution through well-defined interfaces to robot controllers and perception modules. After initial deployment, the same Skill Graph structure supports systematic data collection and closed-loop performance improvement, enabling iterative refinement of skills and their composition. We demonstrate how this approach unifies system configuration, execution, evaluation, and learning within a single representation, providing a scalable pathway toward adaptive and reusable robotic assembly systems. The code is at https://github.com/intelligent-control-lab/AIDF.

cs.RO↗

arXiv2025

Prompt-to-Product: Generative Assembly via Bimanual Manipulation

Creating assembly products demands significant manual effort and expert knowledge in 1) designing the assembly and 2) constructing the product. This paper introduces Prompt-to-Product, an automated pipeline that generates real-world assembly products from natural language prompts. Specifically, we leverage LEGO bricks as the assembly platform and automate the process of creating brick assembly structures. Given the user design requirements, Prompt-to-Product generates physically buildable brick designs, and then leverages a bimanual robotic system to construct the real assembly products, bringing user imaginations into the real world. We conduct a comprehensive user study, and the results demonstrate that Prompt-to-Product significantly lowers the barrier and reduces manual effort in creating assembly products from imaginative ideas.

cs.RO↗

arXiv2025

Benchmarking Shortcutting Techniques for Multi-Robot-Arm Motion Planning

Generating high-quality motion plans for multiple robot arms is challenging due to the high dimensionality of the system and the potential for inter-arm collisions. Traditional motion planning methods often produce motions that are suboptimal in terms of smoothness and execution time for multi-arm systems. Post-processing via shortcutting is a common approach to improve motion quality for efficient and smooth execution. However, in multi-arm scenarios, optimizing one arm's motion must not introduce collisions with other arms. Although existing multi-arm planning works often use some form of shortcutting techniques, their exact methodology and impact on performance are often vaguely described. In this work, we present a comprehensive study quantitatively comparing existing shortcutting methods for multi-arm trajectories across diverse simulated scenarios. We carefully analyze the pros and cons of each shortcutting method and propose two simple strategies for combining these methods to achieve the best performance-runtime tradeoff. Video, code, and dataset are available at https://philip-huang.github.io/mr-shortcut/.

cs.RO↗

arXiv2025

VulBinLLM: LLM-powered Vulnerability Detection for Stripped Binaries

Recognizing vulnerabilities in stripped binary files presents a significant challenge in software security. Although some progress has been made in generating human-readable information from decompiled binary files with Large Language Models (LLMs), effectively and scalably detecting vulnerabilities within these binary files is still an open problem. This paper explores the novel application of LLMs to detect vulnerabilities within these binary files. We demonstrate the feasibility of identifying vulnerable programs through a combined approach of decompilation optimization to make the vulnerabilities more prominent and long-term memory for a larger context window, achieving state-of-the-art performance in binary vulnerability analysis. Our findings highlight the potential for LLMs to overcome the limitations of traditional analysis methods and advance the field of binary vulnerability detection, paving the way for more secure software systems. In this paper, we present Vul-BinLLM , an LLM-based framework for binary vulnerability detection that mirrors traditional binary analysis workflows with fine-grained optimizations in decompilation and vulnerability reasoning with an extended context. In the decompilation phase, Vul-BinLLM adds vulnerability and weakness comments without altering the code structure or functionality, providing more contextual information for vulnerability reasoning later. Then for vulnerability reasoning, Vul-BinLLM combines in-context learning and chain-of-thought prompting along with a memory management agent to enhance accuracy. Our evaluations encompass the commonly used synthetic dataset Juliet to evaluate the potential feasibility for analysis and vulnerability detection in C/C++ binaries. Our evaluations show that Vul-BinLLM is highly effective in detecting vulnerabilities on the compiled Juliet dataset.

cs.CR↗

arXiv2025

APEX-MR: Multi-Robot Asynchronous Planning and Execution for Cooperative Assembly

Compared to a single-robot workstation, a multi-robot system offers several advantages: 1) it expands the system's workspace, 2) improves task efficiency, and, more importantly, 3) enables robots to achieve significantly more complex and dexterous tasks, such as cooperative assembly. However, coordinating the tasks and motions of multiple robots is challenging due to issues, e.g., system uncertainty, task efficiency, algorithm scalability, and safety concerns. To address these challenges, this paper studies multi-robot coordination and proposes APEX-MR, an asynchronous planning and execution framework designed to safely and efficiently coordinate multiple robots to achieve cooperative assembly, e.g., LEGO assembly. In particular, APEX-MR provides a systematic approach to post-process multi-robot tasks and motion plans to enable robust asynchronous execution under uncertainty. Experimental results demonstrate that APEX-MR can significantly speed up the execution time of many long-horizon LEGO assembly tasks by 48% compared to sequential planning and 36% compared to synchronous planning on average. To further demonstrate performance, we deploy APEX-MR in a dual-arm system to perform physical LEGO assembly. To our knowledge, this is the first robotic system capable of performing customized LEGO assembly using commercial LEGO bricks. The experimental results demonstrate that the dual-arm system, with APEX-MR, can safely coordinate robot motions, efficiently collaborate, and construct complex LEGO structures. Our project website is available at https://intelligent-control-lab.github.io/APEX-MR/.

cs.RO↗

arXiv2023

STAMP: Differentiable Task and Motion Planning via Stein Variational Gradient Descent

Planning for sequential robotics tasks often requires integrated symbolic and geometric reasoning. TAMP algorithms typically solve these problems by performing a tree search over high-level task sequences while checking for kinematic and dynamic feasibility. This can be inefficient because, typically, candidate task plans resulting from the tree search ignore geometric information. This often leads to motion planning failures that require expensive backtracking steps to find alternative task plans. We propose a novel approach to TAMP called Stein Task and Motion Planning (STAMP) that relaxes the hybrid optimization problem into a continuous domain. This allows us to leverage gradients from differentiable physics simulation to fully optimize discrete and continuous plan parameters for TAMP. In particular, we solve the optimization problem using a gradient-based variational inference algorithm called Stein Variational Gradient Descent. This allows us to find a distribution of solutions within a single optimization run. Furthermore, we use an off-the-shelf differentiable physics simulator that is parallelized on the GPU to run parallelized inference over diverse plan parameters. We demonstrate our method on a variety of problems and show that it can find multiple diverse plans in a single optimization run while also being significantly faster than existing approaches.

cs.RO↗

arXiv2023

Field Testing of a Stochastic Planner for ASV Navigation Using Satellite Images

We introduce a multi-sensor navigation system for autonomous surface vessels (ASV) intended for water-quality monitoring in freshwater lakes. Our mission planner uses satellite imagery as a prior map, formulating offline a mission-level policy for global navigation of the ASV and enabling autonomous online execution via local perception and local planning modules. A significant challenge is posed by the inconsistencies in traversability estimation between satellite images and real lakes, due to environmental effects such as wind, aquatic vegetation, shallow waters, and fluctuating water levels. Hence, we specifically modelled these traversability uncertainties as stochastic edges in a graph and optimized for a mission-level policy that minimizes the expected total travel distance. To execute the policy, we propose a modern local planner architecture that processes sensor inputs and plans paths to execute the high-level policy under uncertain traversability conditions. Our system was tested on three km-scale missions on a Northern Ontario lake, demonstrating that our GPS-, vision-, and sonar-enabled ASV system can effectively execute the mission-level policy and disambiguate the traversability of stochastic edges. Finally, we provide insights gained from practical field experience and offer several future directions to enhance the overall reliability of ASV navigation systems.

cs.RO↗