Quick Start

Object Pushing (Simulation)

Multi-object herding with orbital cage constraints in parallel ManiSkill environments.

python examples/tasks/object_pushing/main.py

Object Pushing (Diffusion World Model)

Cage-constrained planning using a learned diffusion world model. Requires downloading model weights (see Model Weights).

python examples/tasks/object_pushing/main_pixel.py

With iterative feedback loop between diffusion planning and real execution:

python examples/tasks/object_pushing/main_pixel_feedback.py

Object Picking

MPPI-based trajectory optimization with 3D cage constraints for pick-and-place.

python examples/tasks/object_picking/main.py

Object Catching

Plate-based ball catching with optional CAGE enhancement.

# Baseline (direct policy)
python examples/tasks/object_catching/main.py --num_samples 0

# CAGE mode (8 candidate actions, 16 DRIS copies)
python examples/tasks/object_catching/main.py --num_samples 8 --num_objs_tsip 16

ManiSkill Default Tasks

PushCube, PickCube, and PushT with PPO policies and optional CAGE enhancement.

python examples/tasks/maniskill_defaults/main.py --task PushCube-v1
python examples/tasks/maniskill_defaults/main.py --task PickCube-v1
python examples/tasks/maniskill_defaults/main.py --task PushT-v0

Use --num_samples 0 for baseline (direct policy) or --num_samples 16 for CAGE mode.

---

For detailed task descriptions and component configurations, see Runnable Examples and ManiSkill Defaults. To create your own task, see Custom Tasks.