Motion Generation
Motion generation through path planning, trajectory optimization, and novel robot learning approaches.
Eric Heiden
Research Scientist
My research interests include autonomous robots, simulators, and motion planning.
Publications
Grasp'D: Differentiable Contact-rich Grasp Synthesis for Multi-fingered Hands
Synthesize contact-rich grasps for multi-fingered hands via differentiable simulation
Bench-MR: A Motion Planning Benchmark for Wheeled Mobile Robots
Planning smooth and energy-efficient motions for wheeled mobile robots is a central task in applications ranging from autonomous …
Scaling simulation-to-real transfer by learning composable robot skills
We present a strategy for simulation-to-real transfer, which builds on recent advances in robot skill decomposition. Rather than …
Auto-conditioned Recurrent Mixture Density Networks for Learning Generalizable Robotic Manipulation Skills
Personal robots assisting humans must perform complex manipulation tasks that are typically difficult to specify in traditional motion …
