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Trajectory Optimization

The main entry point is generate_trajectory. It adapts a demonstration trajectory to a requested start and goal pose, then reports which backend was requested and which solver path actually ran.

Backend Selection

Use backend="auto" for normal single-trajectory generation. It prefers FATROP when the optional Rockit/FATROP stack is importable. Fallback to slower IPOPT or interpolation is disabled by default; set allow_fallback=True only when an exploratory run should continue after a preferred backend is unavailable.

Backend Use when Notes
auto Production default Uses FATROP when available.
fatrop Require the structured OCP solver Raises unless FATROP succeeds or allow_fallback=True.
cusadi Boundary adaptation plus fixed-horizon CusADi decode Exact GPU decode is optional and falls back to CPU by default.
ipopt Legacy CasADi/IPOPT behavior Useful for comparisons and fallback studies.
interpolation Deterministic resample/decode baseline No nonlinear solver dependency. 
from dhb_xr.optimization import generate_trajectory

result = generate_trajectory(
    demo_positions,
    demo_quaternions,
    pose_target_init={"position": start_pos, "quaternion": start_quat},
    pose_target_final={"position": goal_pos, "quaternion": goal_quat},
    traj_length=100,
    backend="auto",
)

print(result["requested_backend"], result["solver"])

CusADi Decode Policy

CusADi decode is fixed-horizon by design. DHB-XR ships artifacts and generated CUDA source for sample horizons 50, 80, 100, 150, and 200. Build compiled libraries only when the machine needs GPU decode:

python -m dhb_xr.optimization.build_cusadi_decode --horizons 50 80 100 150 200

Runtime controls:

  • cusadi_decode="auto": use GPU decode when all assets are available, otherwise use CPU fallback.
  • cusadi_decode="gpu_required": require a matching compiled library and CUDA execution.
  • cusadi_decode_horizon="auto": select the exact sample horizon from traj_length.
  • cusadi_decode_horizon=100: require a specific fixed sample horizon.
  • cusadi_decode_fallback="cpu": default CPU fallback.
  • cusadi_decode_fallback="error": raise instead of falling back.
  • cusadi_decode_library_dir=...: load prebuilt libraries from a custom directory instead of the default dhb_xr cache.
result = generate_trajectory(
    demo_positions,
    demo_quaternions,
    pose_target_init={"position": start_pos, "quaternion": start_quat},
    pose_target_final={"position": goal_pos, "quaternion": goal_quat},
    traj_length=100,
    backend="cusadi",
    cusadi_decode="gpu_required",
    cusadi_decode_horizon=100,
)

print(result["cusadi_decode"])
print(result["cusadi_decode_library_path"])

The result metadata includes requested_backend, solver, optimizer_solver, cusadi_decode, cusadi_decode_requested_horizon, cusadi_decode_artifact_horizon, cusadi_decode_artifact_path, cusadi_decode_library_path, and cusadi_decode_fallback_reason when that path runs.

See GPU Decode for install, build, cache, and troubleshooting details.

API Surface

Public facade:

  • dhb_xr.optimization.generate_trajectory
  • dhb_xr.optimization.get_optimizer

FATROP path:

  • dhb_xr.optimization.fatrop_solver.generate_trajectory_fatrop
  • dhb_xr.optimization.fatrop_solver.FatropTrajectoryGenerator
  • dhb_xr.optimization.fatrop_solver.ConstrainedTrajectoryGenerator

CusADi path:

  • dhb_xr.optimization.cusadi_solver.CusadiDecodeSpec
  • dhb_xr.optimization.cusadi_solver.CusadiTrajectoryOptimizer
  • dhb_xr.optimization.cusadi_solver.batched_decode_dhb_dr
  • dhb_xr.optimization.build_cusadi_decode.build_cusadi_decode_libraries