LeCAR-Lab/ASAP

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

2100 stars · 197 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

ASAP is a two-stage framework for training agile humanoid whole-body skills by aligning simulation and real-world physics. It pre-trains motion tracking policies in simulation, then trains a delta (residual) action model from real-world data to compensate for dynamics mismatch and fine-tune policies for sim2real deployment.

Use cases

• train humanoid robots to perform agile whole-body motions like jumps and kicks
• close the sim-to-real gap for humanoid locomotion policies
• retarget human motion capture data (AMASS/SMPL) to arbitrary humanoid robots
• deploy motion tracking policies on real humanoid hardware
• fine-tune simulation-trained policies with a residual dynamics model

When to choose

• you are doing humanoid robotics research on agile whole-body skills
• you need a sim2real pipeline built on IsaacGym, IsaacSim, or Genesis
• you want to retarget SMPL/AMASS human motions to a humanoid robot

When to avoid

• you need a production-ready robotics middleware rather than research code
• you work with non-humanoid robots
• you lack GPU hardware or NVIDIA Isaac simulator access

Facets

library · maturity active

reinforcement-learning simulation robotics machine-learning robotics reinforcement-learning simulation machine-learning python humanoid sim2real motion-tracking isaacgym isaacsim genesis motion-retargeting delta-action-model research-code linux gpu

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markdown · JSON · MCP: product_card(name="LeCAR-Lab/ASAP")