CusADi2: Latency-Optimized Batch Computation of Robot Kinematics, Jacobians, and Hessians on the GPU

Key points

• Existing frameworks utilize modern GPU hardware primarily to parallelize across batch instances, thereby optimizing throughput but not latency.1
• We present CusADi2, a software library that reduces the batch evaluation latency by additionally parallelizing computation within each batch instance.1
• We benchmark forward kinematics, Jacobians, and Hessians for all 67 robots in the MuJoCo Menagerie, including arms, hands, quadrupeds, humanoids, and biomechanical models, against MuJoCo XLA, MuJoCo Warp, frax, CusADi, and CasADi.1
• We also demonstrate the performance advantage of CusADi2 in a sampling-based control application.1

Sources (1)

• [1]CusADi2: Latency-Optimized Batch Computation of Robot Kinematics, Jacobians, and Hessians on the GPU
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 18, 07:03 PM

  Trajectory optimization methods, from sampling-based zero-order to gradient-based first- and second-order approaches, require many evaluations of dynamical system functions, i.e., functions derived from a robot's dynamical model, such as forward kinematics and their Jacobians and Hessians.

  Existing frameworks utilize modern GPU hardware primarily to parallelize across batch instances, thereby optimizing throughput but not latency.

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