We lifted 5 functions out of this paper's own repositories and ran 5 of them in a sandbox. "Ran" means the function executed on a synthesized input and returned a value. It is not a reproduction of the paper's results.
| Repository | Role | Ran |
|---|---|---|
| HybridRobotics/GenLoco | canonical | 5 of 5 |
| Function | Status | Where it lives |
|---|---|---|
| compute_constraint_matrix | Ran | HybridRobotics/GenLoco/mpc_controller/qp_torque_optimizer.py code served (permissive licence) · get_code("d41e25b12844e85e") |
| compute_contact_force_projection_matrix | Ran | HybridRobotics/GenLoco/mpc_controller/model_predictive_control.py code served (permissive licence) · get_code("a72d9c3ebd9af4d3") |
| compute_mass_matrix | Ran | HybridRobotics/GenLoco/mpc_controller/qp_torque_optimizer.py code served (permissive licence) · get_code("1679293dcb201594") |
| compute_objective_matrix | Ran | HybridRobotics/GenLoco/mpc_controller/qp_torque_optimizer.py code served (permissive licence) · get_code("cabb8adb73e440aa") |
| plan_foot_contact_force | Ran | HybridRobotics/GenLoco/mpc_controller/model_predictive_control.py code served (permissive licence) · get_code("0f33b02ff9ef88b9") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
Recent years have seen a surge in commercially-available and affordable quadrupedal robots, with many of these platforms being actively used in research and industry. As the availability of legged robots grows, so does the need for controllers that enable these robots to perform useful skills. However, most learning-based frameworks for controller development focus on training robot-specific controllers, a process that needs to be repeated for every new robot. In this work, we introduce a framework for training generalized locomotion (GenLoco) controllers for quadrupedal robots. Our framework synthesizes general-purpose locomotion controllers that can be deployed on a large variety of quadrupedal robots with similar morphologies. We present a simple but effective morphology randomization method that procedurally generates a diverse set of simulated robots for training. We show that by training a controller on this large set of simulated robots, our models acquire more general control strategies that can be directly transferred to novel simulated and real-world robots with diverse morphologies, which were not observed during training.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2209.05309")
get_code_for_paper("2209.05309")
have("2209.05309")
Connect an agent — have() is free.