Differentiable Particle Optimization for Fast Sequential Manipulation
🤖 AI Summary
For sequential robotic manipulation involving multiple interacting objects, existing methods struggle to generate collision-free trajectories that satisfy geometric constraints and dynamic feasibility in real time within high-dimensional configuration spaces. This paper introduces SPaSM—the first fully GPU-parallel, end-to-end differentiable particle-based optimization framework. SPaSM unifies sampling, collision checking, constraint compilation, and gradient-based optimization within two highly optimized CUDA kernels, eliminating CPU–GPU communication bottlenecks and enabling joint optimization of robot trajectories and object placements. Evaluated on complex benchmarks, SPaSM achieves millisecond-scale planning (average <10 ms) with 100% success rate, outperforming state-of-the-art methods by up to 4000× in speed. It is the first approach to enable real-time, high-reliability sequential manipulation planning in large-scale, high-dimensional scenarios.
Application Category
📝 Abstract
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 $ extbf{milliseconds}$ with a 100% success rate; a $4000 imes$ speedup compared to existing approaches.
Problem
Research questions and friction points this paper is trying to address.
Optimizing sequential robot manipulation trajectories under geometric constraints
Overcoming computational bottlenecks in real-time collision-free path planning
Enabling GPU-parallelized optimization for joint object placement and motion feasibility
Innovation
Methods, ideas, or system contributions that make the work stand out.
Fully GPU-parallelized framework for trajectory optimization
Two-stage particle optimization strategy for constraints
Jointly optimizes object placements and robot trajectories
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