Hdmove2 [top] <2024>

[ q^* = \arg\min_q \in Q | q - D(z^*) |^2 \quad \texts.t. \quad q \in Q_free ]

[ \mathcalJ[\tau] = \int_0^T \left( \underbrace \dot\tau(t) \textkinetic energy + \lambda_1 \underbrace \dddot\tau(t) \textjerk + \lambda_2 \underbracec_obs(\tau(t))_\textcollision cost \right) dt ] hdmove2

[3] A. Sterling and J. Liu, "hdmove1: Latent motion primitives for high-DoF planning," arXiv preprint arXiv:2401.04567 , 2024. [ q^* = \arg\min_q \in Q | q - D(z^*) |^2 \quad \texts

The lower level is solved using a fast alternating direction method of multipliers (ADMM) that converges in under 5 ms for ( n \leq 128 ). Re-planning is triggered when: Ratliff, M

[2] N. Ratliff, M. Zucker, J. A. Bagnell, and S. Srinivasa, "CHOMP: Gradient optimization algorithms for efficient motion planning," IEEE International Conference on Robotics and Automation (ICRA) , 2009, pp. 1292–1299.

[5] T. P. Lillicrap et al., "Continuous control with deep reinforcement learning," International Conference on Learning Representations (ICLR) , 2016.

| Algorithm | Success Rate (Bench B) | Planning Time (ms) | Cumulative Jerk (m²/s⁵) | Real-time feasible (>30 Hz) | |-----------|------------------------|--------------------|--------------------------|-------------------------------| | RRT* | 0.12 ± 0.05 | 3420 ± 450 | 18.4 ± 3.2 | No | | CHOMP | 0.68 ± 0.12 | 520 ± 85 | 9.2 ± 1.8 | No (for n>30) | | hdmove1 | 0.71 ± 0.10 | 88 ± 12 | 5.3 ± 0.9 | Yes (at 35 Hz) | | | 0.94 ± 0.04 | 41 ± 6 | 1.4 ± 0.3 | Yes (at 95 Hz) |