We lifted 3 functions out of this paper's own repositories and ran 2 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 |
|---|---|---|
| petercorke/robotics-toolbox-python | pwc_unofficial | 2 of 3 |
| Function | Status | Where it lives |
|---|---|---|
| env_arguments | Ran | petercorke/robotics-toolbox-python/src/roboticstoolbox/bin/rtbtool.py code served (permissive licence) · get_code("557161182505f74f") |
| link | Ran | petercorke/robotics-toolbox-python/src/roboticstoolbox/bin/_bintools.py code served (permissive licence) · get_code("9e92563590910a0b") |
| eigdemo | Not yet run | petercorke/robotics-toolbox-python/src/roboticstoolbox/demo/eigdemo.py code served (permissive licence) · get_code("41814955ab618a3a") |
Some links come from the archived Papers with Code dataset (CC BY-SA 4.0): attribution and licence.
We present a novel approach to controlling the instantaneous velocity of a robot end-effector that is able to simultaneously maximise manipulability and avoid joint limits. It operates on non-redundant and redundant robots, which is achieved by adding artificial redundancy in the form of controlled path deviation. We formulate the problem as a quadratic programme and provide an open-source Python implementation that provides solutions in just a few milliseconds. It accepts a robot model expressed using URDF or Denavit-Hartenberg parameterisation. We compare our method to previous work in simulation and on a physical robot.
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2002.11901")
get_code_for_paper("2002.11901")
have("2002.11901")
Connect an agent — have() is free.