Five papers accepted to IROS 2026!
Created in June 17, 2026
The CoMMA Lab will be presenting seven papers at IROS 2026!
2026
- Vectorizing Projection in Manifold-Constrained Motion Planning for Real-Time Whole-Body ControlIn IEEE/RSJ International Conference on Intelligent Robots and SystemsTo Appear
Many robot planning tasks require satisfaction of one or more constraints throughout the entire trajectory. For geometric constraints, manifold-constrained motion planning algorithms are capable of planning collision-free path between start and goal configurations on the constraint submanifolds specified by task. Current state-of-the-art methods can take tens of seconds to solve these tasks for complex systems such as humanoid robots, making real-world use impractical, especially in dynamic settings. Inspired by recent advances in hardware accelerated motion planning, we present a CPU SIMD-accelerated manifold-constrained motion planner that revisits projection-based constraint satisfaction through the lens of parallelization. By transforming relevant components into parallelizable structures, we use SIMD parallelism to plan constraint satisfying solutions. Our approach achieves up to 100-1000x speed-ups over the state-of-the-art, making real-time constrained motion planning feasible for the first time. We demonstrate our planner on a real humanoid robot and show real-time whole-body quasi-static plan generation.
@inproceedings{iyer2026mcvamp, author = {Iyer, Shrutheesh R. and Chang, I-Chia and Liu, Andrew Z. and Gu, Yan and Kingston, Zachary}, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems}, note = {To Appear}, title = {Vectorizing Projection in Manifold-Constrained Motion Planning for Real-Time Whole-Body Control}, year = {2026} } - HJCD-IK: GPU-Accelerated Inverse Kinematics through Batched Hybrid Jacobian Coordinate DescentIn IEEE/RSJ International Conference on Intelligent Robots and SystemsTo Appear
Inverse Kinematics (IK) is a core problem in robotics, in which joint configurations are found to achieve a desired end-effector pose. Although analytical solvers are fast and efficient, they are limited to systems with low degrees-of-freedom and specific topological structures. Numerical optimization-based approaches are more general, but suffer from high computational costs and frequent convergence to spurious local minima. Recent efforts have explored the use of GPUs to combine sampling and optimization to enhance both the accuracy and speed of IK solvers. We build on this recent literature and introduce HJCD-IK, a GPU-accelerated, sampling-based hybrid solver that combines an orientation-aware greedy coordinate descent initialization scheme with a Jacobian-based polishing routine. This design enables our solver to improve both convergence speed and overall accuracy as compared to the state-of-the-art, consistently finding solutions along the accuracy-latency Pareto frontier and often achieving order-of-magnitude gains. In addition, our method produces a broad distribution of high-quality samples, yielding the lowest maximum mean discrepancy. We release our code open-source for the benefit of the community.
@inproceedings{yasutake2026hjcdik, author = {Yasutake, Cael and Liu, Andrew H. and Kingston, Zachary and Plancher, Brian}, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems}, note = {To Appear}, title = {{HJCD-IK}: {GPU}-Accelerated Inverse Kinematics through Batched Hybrid Jacobian Coordinate Descent}, year = {2026} } - NeHMO: Neural Hamilton-Jacobi Reachability Learning for Decentralized Safe Multi-Agent Motion PlanningIn IEEE/RSJ International Conference on Intelligent Robots and SystemsTo Appear
Safe multi-arm motion planning is a challenging problem in robotics due to its high dimensionality, coupled configuration space, and complex collision constraints. Centralized planners are capable of coordinating all arms but often face scalability limitations, restricting applicability in real-time settings. On the other hand, decentralized methods are scalable and recent deep learning-based approaches have shown promising results. However, these depend on accurate behavior prediction or coordination protocols and may fail when other arms act unpredictably. To address these challenges, we introduce a neural Hamilton-Jacobi Reachability (HJR) learning-based approach to approximate a safety value function that captures worst-case inter-arm safety constraints. We further develop a decentralized trajectory optimization framework that uses the learned HJR representation for real-time planning. The proposed method is scalable and data-efficient, generalizes across multi-manipulator systems, and outperforms state-of-the-art baselines on challenging multi-arm motion planning tasks.
@inproceedings{chen2026nehmo, author = {Chen, Qingyi and Kingston, Zachary and Qureshi, Ahmed H.}, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems}, note = {To Appear}, title = {{NeHMO}: Neural {Hamilton-Jacobi} Reachability Learning for Decentralized Safe Multi-Agent Motion Planning}, year = {2026} } - Fast Asymptotically Optimal Kinodynamic Planning via VectorizationIn IEEE/RSJ International Conference on Intelligent Robots and SystemsTo Appear
Sampling-based motion planners have been shown to be effective for systems with complex kinodynamic constraints and high dimensionality. However, these algorithms struggle to achieve real-time performance, leading to recent efforts to parallelize planning. While GPU-accelerated planners have achieved significant speedups, existing approaches require specialized CUDA programming that limits accessibility and portability. We present Parallel Asymptotically Optimal Kinodynamic RRT (PAKR), a massively parallel kinodynamic planner leveraging JAX and the XLA compiler to achieve GPU acceleration through standard Python tooling. By combining our parallel planner with the AO-x meta-algorithm, we achieve asymptotic optimality through fast iterative replanning. We provide a theoretical analysis of probabilistic completeness, analyze the effects of batch size and branching factor on convergence, and demonstrate scalability to complex dynamics using the MuJoCo-XLA simulator. Experiments show competitive runtimes with state-of-the-art GPU planners and superior solution quality.
@inproceedings{gao2026pakr, author = {Gao, Yitian and Lu, Andrew and Kingston, Zachary}, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems}, note = {To Appear}, title = {Fast Asymptotically Optimal Kinodynamic Planning via Vectorization}, year = {2026} } - CDE: Concept-Driven Exploration for Reinforcement LearningIn IEEE/RSJ International Conference on Intelligent Robots and SystemsTo Appear
Intelligent exploration remains a critical challenge in reinforcement learning (RL), especially in visual control tasks. Unlike low-dimensional state-based RL, visual RL must extract task-relevant structure from raw pixels, making exploration inefficient. We propose Concept-Driven Exploration (CDE), which leverages a pre-trained vision-language model (VLM) to generate object-centric visual concepts from textual task descriptions as weak, potentially noisy supervisory signals. Rather than directly conditioning on these noisy signals, CDE trains a policy to reconstruct the concepts via an auxiliary objective, using reconstruction accuracy as an intrinsic reward to guide exploration toward task-relevant objects. Because the policy internalizes these concepts, VLM queries are only needed during training, reducing dependence on external models during deployment. Across five challenging simulated visual manipulation tasks, CDE achieves efficient, targeted exploration and remains robust to noisy VLM predictions. Finally, we demonstrate real-world transfer by deploying CDE on a Franka Research 3 arm, attaining an 80% success rate in a real-world manipulation task.
@inproceedings{mao2026cde, author = {Mao, Le and Liu, Andrew H. and Zabounidis, Renos and Niu, Yanan and Kingston, Zachary and Campbell, Joseph}, booktitle = {IEEE/RSJ International Conference on Intelligent Robots and Systems}, note = {To Appear}, title = {{CDE}: Concept-Driven Exploration for Reinforcement Learning}, year = {2026} }
We will also be presenting two RA-L papers:
2026
- Efficient Multi-Robot Motion Planning for Manifold-Constrained Manipulators by Randomized Scheduling and Informed Path GenerationIEEE Robotics and Automation Letters
Multi-robot motion planning for high degree-of-freedom manipulators in shared, constrained, and narrow spaces is a complex problem and essential for many scenarios such as construction, surgery, and more. Traditional coupled and decoupled methods either scale poorly or lack completeness, and hybrid methods that compose paths from individual robots together require the enumeration of many paths before they can find valid composite solutions. This paper introduces Scheduling to Avoid Collisions (StAC), a hybrid approach that more effectively composes paths from individual robots by scheduling (adding random stops and coordination motion along each path) and generates paths that are more likely to be feasible by using bidirectional feedback between the scheduler and motion planner for informed sampling. StAC uses 10 to 100 times fewer paths from the low-level planner than state-of-the-art baselines on challenging problems in manipulator cases.
@article{guo2026stac, author = {Guo, Weihang and Kingston, Zachary and Hang, Kaiyu and Kavraki, Lydia E.}, doi = {10.1109/LRA.2026.3662639}, journal = {IEEE Robotics and Automation Letters}, title = {Efficient Multi-Robot Motion Planning for Manifold-Constrained Manipulators by Randomized Scheduling and Informed Path Generation}, year = {2026} }
2025
- Variational Shape Inference for Grasp Diffusion on SE(3)IEEE Robotics and Automation Letters
Grasp synthesis is a fundamental task in robotic manipulation which usually has multiple feasible solutions. Multimodal grasp synthesis seeks to generate diverse sets of stable grasps conditioned on object geometry, making the robust learning of geometric features crucial for success. To address this challenge, we propose a framework for learning multimodal grasp distributions that leverages variational shape inference to enhance robustness against shape noise and measurement sparsity. Our approach first trains a variational autoencoder for shape inference using implicit neural representations, and then uses these learned geometric features to guide a diffusion model for grasp synthesis on the SE(3) manifold. Additionally, we introduce a test-time grasp optimization technique that can be integrated as a plugin to further enhance grasping performance. Experimental results demonstrate that our shape inference for grasp synthesis formulation outperforms state-of-the-art multimodal grasp synthesis methods on the ACRONYM dataset by 6.3%, while demonstrating robustness to deterioration in point cloud density compared to other approaches. Furthermore, our trained model achieves zero-shot transfer to real-world manipulation of household objects, generating 34% more successful grasps than baselines despite measurement noise and point cloud calibration errors.
@article{bukhari2025graspdiff, author = {Bukhari, S. Talha and Agrawal, Kaivalya and Kingston, Zachary and Bera, Aniket}, doi = {10.1109/LRA.2025.3645521}, journal = {IEEE Robotics and Automation Letters}, title = {Variational Shape Inference for Grasp Diffusion on SE(3)}, year = {2025} }