I am a Masters student in the CoMMA lab in the Computer Science Department of Purdue University. I am currently working on learning-based motion planning and trajectory optimization for realtime (millisecond!) planning.
I’m still sorting out my research interests, which broadly involve designing robotic systems and algorithms that can make informed adaptations to unseen and ever-changing conditions in the real world. Sometimes, this involves distilling high level semantic information into executable trajectories and motions.
In the past, I have worked on projects spanning kinodynamic motion planning for wacky situations, distributed multiagent RL, open language querying, and human-robot interaction/intent modelling. I am also interested in how we can make proof-of-concept methods from literature reliable and explainable enough to deploy to commercial robots.
2025
arXiv
Differentiable Particle Optimization for Fast Sequential Manipulation
Sequential robot manipulation tasks require finding collision-free trajectories that satisfy geometric constraints across multiple object interactions in potentially high-dimensional configuration spaces. Solving these problems in real-time and at large scales has remained out of reach due to computational requirements. Recently, GPU-based acceleration has shown promising results, but prior methods achieve limited performance due to CPU-GPU data transfer overhead and complex logic that prevents full hardware utilization. To this end, we present SPaSM (Sampling Particle optimization for Sequential Manipulation), a fully GPU-parallelized framework that compiles constraint evaluation, sampling, and gradient-based optimization into optimized CUDA kernels for end-to-end trajectory optimization without CPU coordination. The method consists of a two-stage particle optimization strategy: first solving placement constraints through massively parallel sampling, then lifting solutions to full trajectory optimization in joint space. Unlike hierarchical approaches, SPaSM jointly optimizes object placements and robot trajectories to handle scenarios where motion feasibility constrains placement options. Experimental evaluation on challenging benchmarks demonstrates solution times in the realm of milliseconds with a 100% success rate; a 4000x speedup compared to existing approaches.
@misc{chen2025spasm,title={Differentiable Particle Optimization for Fast Sequential Manipulation},author={Chen, Lucas and Iyer, Shrutheesh R. and Kingston, Zachary},eprint={2510.07674},archiveprefix={arXiv},primaryclass={cs.RO},year={2025},note={Under Review},}
arXiv
Parallel Simulation of Contact and Actuation for Soft Growing Robots
Soft growing robots, commonly referred to as vine robots, have demonstrated remarkable ability to interact safely and robustly with unstructured and dynamic environments. It is therefore natural to exploit contact with the environment for planning and design optimization tasks. Previous research has focused on planning under contact for passively deforming robots with pre-formed bends. However, adding active steering to these soft growing robots is necessary for successful navigation in more complex environments. To this end, we develop a unified modeling framework that integrates vine robot growth, bending, actuation, and obstacle contact. We extend the beam moment model to include the effects of actuation on kinematics under growth and then use these models to develop a fast parallel simulation framework. We validate our model and simulator with real robot experiments. To showcase the capabilities of our framework, we apply our model in a design optimization task to find designs for vine robots navigating through cluttered environments, identifying designs that minimize the number of required actuators by exploiting environmental contacts. We show the robustness of the designs to environmental and manufacturing uncertainties. Finally, we fabricate an optimized design and successfully deploy it in an obstacle-rich environment.
@misc{gaochen2025actsim,title={Parallel Simulation of Contact and Actuation for Soft Growing Robots},author={Gao, Yitian and Chen, Lucas and Bhovad, Priyanka and Wang, Sicheng and Kingston, Zachary and Blumenschein, Laura H.},eprint={2509.15180},archiveprefix={arXiv},primaryclass={cs.RO},year={2025},note={Under Review},}
RoboSoft
Physics-Grounded Differentiable Simulation for Soft Growing Robots
Soft-growing robots (i.e., vine robots) are a promising class of soft robots that allow for navigation and growth in tightly confined environments. However, these robots remain challenging to model and control due to the complex interplay of the inflated structure and inextensible materials, which leads to obstacles for autonomous operation and design optimization. Although there exist simulators for these systems that have achieved qualitative and quantitative success in matching high-level behavior, they still often fail to capture realistic vine robot shapes using simplified parameter models and have difficulties in high-throughput simulation necessary for planning and parameter optimization. We propose a differentiable simulator for these systems, enabling the use of the simulator "in-the-loop" of gradient-based optimization approaches to address the issues listed above. With the more complex parameter fitting made possible by this approach, we experimentally validate and integrate a closed-form nonlinear stiffness model for thin-walled inflated tubes based on a first-principles approach to local material wrinkling. Our simulator also takes advantage of data-parallel operations by leveraging existing differentiable computation frameworks, allowing multiple simultaneous rollouts. We demonstrate the feasibility of using a physics-grounded nonlinear stiffness model within our simulator, and how it can be an effective tool in sim-to-real transfer. We provide our implementation open source.
@inproceedings{chengao2025diffsim,title={Physics-Grounded Differentiable Simulation for Soft Growing Robots},author={Chen, Lucas and Gao, Yitian and Wang, Sicheng and Fuentes, Francesco and Blumenschein, Laura H. and Kingston, Zachary},booktitle={IEEE-RAS International Conference on Soft Robotics},pages={1--8},year={2025},doi={10.1109/RoboSoft63089.2025.11020809},}
