1. Project by: Qi-Xing & Samir Menon

2. Motion Planning for the Human Hand Generate Hand Skeleton Define Configuration Space Sample Configuration Space for Milestones & Collisions Connect Adjacent Configurations User defines two poses – Find Path & Smoothen to get Realistic Motion θiθi θ2θ2 θ1θ1θ20 Find Parametrization Vector, Θ{θ1, θ2..}

3. The Human Hand Motion is induced by the application of musculo- skeletal control We demonstrate planned motion of a human hand Simulated hand has a 20 degree of freedom skeleton Control is applied to the joint angles of the skeleton Planning takes place in 20-dimensional joint configuration space The planned path is executed in a simulated model of the human hand

4. The Hand Skeleton The human hand may be modeled using a 20 DoF skeleton parameterization Configuration of the human hand is represented by a 20 dimensional joint angle vector Hand Space

5. Modeling the Hand ‘Hand-space’ models an actual human hand The hand is represented by a mesh representation of a laser scanned hand The parameterization allows the emulation of a real hand Hand Space Configuration

6. Configuration Space Hand motion is in 20 dimensional configuration space along the planned path θ20 θ2θ2 θ0θ0 θ1θ1 Θ1Θ1 Θ5Θ5 Path of Motion Disallowed Hand Config = C–Space Obstacle Θ2Θ2 Θ3Θ3 Θ4Θ4 Θi={θ1, θ2,…, θ20} Represents a hand configuration θiθi Milestones = Sampled Hand Configuration

7. Uniform Sampling A uniformly random sampler θ20 θ2θ2 θ0θ0 θ1θ1 θiθi

8. Adaptive-Gaussian-Random Sampling An adaptive gaussian sampler θ20 θ2θ2 θ0θ0 θ1θ1 θiθi Random Sample Gaussian Sample Adaptive Sample

9. Connecting Samples Obtain a roadmap in the form of a search graph Connect each sample to 10 closest samples and check for collision Reject connections with collisions θ20 θ2θ2 θ0θ0 θ1θ1 θiθi

10. Collision Detection Strategy θ20 θ2θ2 θ0θ0 θ1θ1 θiθi Collision!!! Path Added To Roadmap!

11. Planning Hand Motion Add start and goal configuration nodes to graph Search for a path in the graph θ20 θ2θ2 θ0θ0 θ1θ1 θiθi Start Goal Resulting Path is Jerky due to imperfect sampling!!

12. Planning Hand Motion (contd.) Video of jerky motion

13. Smoothing Motion θ20 θ2θ2 θ0θ0 θ1θ1 θiθi Start Goal Smooth Path is obtained!!

14. Smoothing Motion (contd.) Video of smooth motion

15. Demo System demo Eg.1 Eg.2 Eg.4 Eg.3

16. Results: Sampling

17. Results: Smoothing Smoothed Path Length Smoothed Milestones Time (sec) Unsmoothed Path Length Unsmoothed Milestones Time (sec) Eg.13.4021.6506.5850.650 Eg.24.1120.7356.1650.698 Eg.32.3430.6052.4540.585 Eg.42.9530.5903.0030.565

18. Discussion Smoothing the path greatly improves motion quality Adaptive Gaussian Sampling can drastically reduce the required samples but it also requires more precomputation Straight line motion in higher dimensional space produces better quality than curved or spline motion.

19. Future Work Areas for improvement: The project may be extended to involve: Control applied to muscular configuration space Improved skeleton that closely matches a real hand System dynamics such as inertia and damping