Sketch-to-Skill: Bootstrapping Robot Learning with Human Drawn Trajectory Sketches
Training robotic manipulation policies traditionally requires numerous demonstrations and/or environmental rollouts. While recent Imitation Learning (IL) and Reinforcement Learning (RL) methods have reduced the number of required demonstrations, they still rely on expert knowledge to collect high-quality data, limiting scalability and accessibility.
Evidence notes
- Experiments use Robotiq Hand-E Adaptive Gripper as the physical evaluation platform.
- The evaluation connects the reported method to Robotiq Hand-E Adaptive Gripper hardware in manipulation and dexterity tasks.
Company context
Canadian collaborative-robot tooling company supplying adaptive grippers, vacuum grippers, force/torque sensing and related cobot components.