Robot dexterity remains an open challenge in robotics that has the potential to transform manufacturing, healthcare, and daily life. Robots that safely and robustly interact with unstructured environments must combine compliant hardware with models and planners that tolerate uncertainty. Additionally, if robust robot dexterity is to be realized outside of research labs, it must be accessible to a broad audience, with low-cost hardware and open-source software.

This thesis advances dexterous manipulation with soft, tendon-driven hands by integrating new fabrication and control methods, data-driven models of manipulation capabilities, a fast algorithm for robust grasp synthesis, and a first-of-its-kind accessible experimental platform.

I introduce fully soft foam hands actuated by tendons routed on textile skins. I detail a simple molding-and-casting pipeline, validate a soft-body simulation framework, compare inversekinematics control strategies, and optimize nontrivial tendon routings. I further report a user study on human-designed routings, demonstrations of power/precision grasps and in-hand manipulation, sub-millimeter repeatability, and year-long durability, alongside an analysis of limitations (e.g., routing through foam, sensing, and sim-to-real gaps).

To tackle the challenge of planning with soft hands, I develop data-driven models of manipulation capabilities that capture the inherent uncertainty and redundancy of soft hands. Additionally, I demonstrate a fast, anytime method to compute globally optimal Independent Contact Regions (ICRs) by iteratively building an incremental Delaunay triangulation over grasp configuration space. I show that ICRs guide simple policies that remain robust to real-world uncertainties in object size, pose, and geometry.

Finally, to promote accessibility, I contribute the DexKit system, a low-cost, anthropomorphic system (12 actuated DoF hand on a 4-DoF gantry) that can be built for under $2000.