GPU-Accelerated Continuous-Time Successive Convexification for Contact-Implicit Legged Locomotion

• URL: http://arxiv.org/abs/2604.09993v1
• Date: Sat, 11 Apr 2026 02:40:29 GMT
• Title: GPU-Accelerated Continuous-Time Successive Convexification for Contact-Implicit Legged Locomotion
• Abstract summary: Contact-implicit trajectory optimization (CITO) enables the automatic discovery of contact sequences.<n>We introduce integral cross-compITOlementarity constraints, which eliminate the risk of missing contact events between discretization nodes.<n>We show that the resulting framework, contact-implicit successive convexification (ci-SCvx), models full multibody dynamics in maximal coordinates.
• Score: 0.4588028371034407
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Contact-implicit trajectory optimization (CITO) enables the automatic discovery of contact sequences, but most methods rely on fine time discretization to capture all contact events accurately, which increases problem size and runtime while tying solution quality to grid resolution. We extend the recently proposed sequential convex programming (SCP) approach for trajectory optimization, continuous-time successive convexification (ct-SCvx), to CITO by introducing integral cross-complementarity constraints, which eliminate the risk of missing contact events between discretization nodes while preserving the flexibility of contact mode changes. The resulting framework, contact-implicit successive convexification (ci-SCvx), models full multibody dynamics in maximal coordinates, including stick-slip friction and partially elastic impacts. To handle complementarity constraints, we embed a backtracking homotopy scheme within SCP for reliable convergence. We implement this framework in a stand-alone Python software, leveraging JAX for GPU acceleration and a custom canonical-form parser for the convex subproblems of SCP to avoid the overhead of general-purpose modeling tools such as CVXPY. We demonstrate ci-SCvx on diverse legged-locomotion tasks. In particular, we validate the approach in MuJoCo with the Gymnasium HalfCheetah model against the MuJoCo MPC baseline, showing that a tracking simulation with the optimized torque profiles from ci-SCvx produces physically consistent trajectories with lesser energy consumption. We also show that the resulting software achieves faster solve times than existing state-of-the-art SCP implementations by over an order of magnitude, thereby demonstrating a practically important contribution to scalable real-time trajectory optimization.

Related papers

• GASP: GPU-Accelerated Safe Planner for Real-Time Collision-Aware Motion Generation with Latent Trajectory Sampling [1.2669530824264628]
  We present GASP, a GPU-Accelerated Safe Planner for real-time, collision-aware joint-space motion generation in known environments.<n>GASP combines a clamped B-spline trajectory parameterization with a convolutional residual neural network that predicts the free interior control points.<n>We validate GASP as an online motion-generation module, where it achieves analytical-level success rates with high collision-aware feasibility.
  arXiv  Detail & Related papers  (2026-08-05T09:18:16Z)
• Biconvex Optimization for Smooth Minimum-Time Trajectories around Convex Obstacles [49.315843050082925]
  We present a bi approach for minimum-time motion planning convex obstacles that is guaranteed to converge.<n>We find that the proposed method produces high-quality trajectories with times to produce state-of-the-art decomposition of variables.
  arXiv  Detail & Related papers  (2026-08-03T19:48:24Z)
• A Task-Space Receding Horizon Controller for Fast Collision Avoidance [0.08594140167290099]
  Real-time collision avoidance for robotic manipulators requires fast reactions to unexpected obstacle motion.<n>Full model predictive control can provide this foresight, but its online cost may grow quickly with horizon length, model fidelity, and the number of active geometric constraints.<n>We present a task-space receding-horizon controller that uses a short contact-consistent rollout to generate a terminal kinematic reference.
  arXiv  Detail & Related papers  (2026-07-17T08:13:12Z)
• Fast Asymptotically Optimal Kinodynamic Planning via Vectorization [0.764671395172401]
  We present Parallel Asymptotically Optimal Kinodynamic RRTPAKR, a massively parallel kinodynamic planner leveraging JAX and the XLA compiler.<n>By combining our parallel planner with the AO-x meta-algorithm, we achieve optimality through fast iterative replanning.
  arXiv  Detail & Related papers  (2026-07-04T19:07:03Z)
• Planning-aligned Token Compression for Long-Context Autonomous Driving [95.59023657139208]
  We propose a planning-aligned working memory framework built on conditional VQ-VA.<n>Compression is conditioned on both historical trajectory and a learned planning intent.<n>We evaluate on high-signal dynamic scenarios where historical context is most critical for behavior correctness.
  arXiv  Detail & Related papers  (2026-06-05T17:16:21Z)
• CADGrasp: Learning Contact and Collision Aware General Dexterous Grasping in Cluttered Scenes [25.832383872036274]
  Dexterous grasping in cluttered environments presents substantial challenges due to the high degrees of freedom of dexterous hands.<n>We propose CADGrasp, a two-stage algorithm for general dexterous grasping using single-view point cloud inputs.
  arXiv  Detail & Related papers  (2026-01-21T14:43:29Z)
• A Fast Semidefinite Convex Relaxation for Optimal Control Problems With Spatio-Temporal Constraints [22.24436694865408]
  Solving optimal control problems (OCs) of autonomous agents is essential in applications ranging from driving autonomous vehicles to self-driving cars.<n>Most of the OCPs are nonpointing due to the coupling between the event timing and the trajectory.<n>We propose a time-scaling direct multiple shooting scheme that partitions the horizon into segments with time constraints.
  arXiv  Detail & Related papers  (2026-01-06T14:38:19Z)
• Towards Robust and Generalizable Continuous Space-Time Video Super-Resolution with Events [71.2439653098351]
  Continuous space-time video super-STVSR has garnered increasing interest for its capability to reconstruct high-resolution and high-frame-rate videos at arbitrary temporal scales.<n>We present EvEnhancer, a novel approach that marries unique properties of high temporal and high dynamic range encapsulated in event streams.<n>Our method achieves state-of-the-art performance on both synthetic and real-world datasets, while maintaining generalizability at OOD scales.
  arXiv  Detail & Related papers  (2025-10-04T15:23:07Z)
• Bidirectional Feature-aligned Motion Transformation for Efficient Dynamic Point Cloud Compression [97.66080040613726]
  We propose a Bidirectional Feature-aligned Motion Transformation (Bi-FMT) framework that implicitly models motion in the feature space.<n>Bi-FMT aligns features across both past and future frames to produce temporally consistent latent representations.<n>We show Bi-FMT surpasses D-DPCC and AdaDPCC in both compression efficiency and runtime.
  arXiv  Detail & Related papers  (2025-09-18T03:51:06Z)
• Adaptive Deadline and Batch Layered Synchronized Federated Learning [66.93447103966439]
  Federated learning (FL) enables collaborative model training across distributed edge devices while preserving data privacy, and typically operates in a round-based synchronous manner.<n>We propose ADEL-FL, a novel framework that jointly optimize per-round deadlines and user-specific batch sizes for layer-wise aggregation.
  arXiv  Detail & Related papers  (2025-05-29T19:59:18Z)
• Space-Time Graphs of Convex Sets for Multi-Robot Motion Planning [2.3416394753138037]
  Multi-Robot Motion Planning (MRMP) is a problem of computing problem of collision-free trajectories for multiple robots in continuous environments.<n>We propose Space-Time Graphs of Convex Sets (ST-GCS), a novel planner that systematically covers the collision-free space-time domain with convex sets instead of relying on random sampling.<n>We also propose Convex Decomposition (ECD) to "reserve" trajectories astemporal obstacles, addressing maintaining a collision-free space-time graph sets for subsequent planning.
  arXiv  Detail & Related papers  (2025-03-01T18:28:57Z)
• Sparser is Faster and Less is More: Efficient Sparse Attention for Long-Range Transformers [58.5711048151424]
  We introduce SPARSEK Attention, a novel sparse attention mechanism designed to overcome computational and memory obstacles. Our approach integrates a scoring network and a differentiable top-k mask operator, SPARSEK, to select a constant number of KV pairs for each query. Experimental results reveal that SPARSEK Attention outperforms previous sparse attention methods.
  arXiv  Detail & Related papers  (2024-06-24T15:55:59Z)
• DiffPD: Differentiable Projective Dynamics with Contact [65.88720481593118]
  We present DiffPD, an efficient differentiable soft-body simulator with implicit time integration. We evaluate the performance of DiffPD and observe a speedup of 4-19 times compared to the standard Newton's method in various applications.
  arXiv  Detail & Related papers  (2021-01-15T00:13:33Z)
• Pushing the Envelope of Rotation Averaging for Visual SLAM [69.7375052440794]
  We propose a novel optimization backbone for visual SLAM systems. We leverage averaging to improve the accuracy, efficiency and robustness of conventional monocular SLAM systems. Our approach can exhibit up to 10x faster with comparable accuracy against the state-art on public benchmarks.
  arXiv  Detail & Related papers  (2020-11-02T18:02:26Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.