Nancy S. Pollard

Dominik Bauer, Cornelia Bauer, Arjun Lakshmipathy et al.

The design and fabrication of soft robot hands is still a time-consuming and difficult process. Advances in rapid prototyping have accelerated the fabrication process significantly while introducing new complexities into the design process. In this work, we present an approach that utilizes novel low-cost fabrication techniques in conjunction with design tools helping soft hand designers to systematically take advantage of multi-material 3D printing to create dexterous soft robotic hands. While very low cost and lightweight, we show that generated designs are highly durable, surprisingly strong, and capable of dexterous grasping.

Arjun Lakshmipathy, Dominik Bauer, Cornelia Bauer et al.

We present a novel method for the direct transfer of grasps and manipulations between objects and hands through utilization of contact areas. Our method fully preserves contact shapes, and in contrast to existing techniques, is not dependent on grasp families, requires no model training or grasp sampling, makes no assumptions about manipulator morphology or kinematics, and allows user control over both transfer parameters and solution optimization. Despite these accommodations, we show that our method is capable of synthesizing kinematically feasible whole hand poses in seconds even for poor initializations or hard to reach contacts. We additionally highlight the method's benefits in both response to design alterations as well as fast approximation over in-hand manipulation sequences. Finally, we demonstrate a solution generated by our method on a physical, custom designed prosthetic hand.

Michael C. Koval, David Hsu, Nancy S. Pollard et al.

This work addresses the challenge of a robot using real-time feedback from contact sensors to reliably manipulate a movable object on a cluttered tabletop. We formulate contact manipulation as a partially observable Markov decision process (POMDP) in the joint space of robot configurations and object poses. The POMDP formulation enables the robot to actively gather information and reduce the uncertainty on the object pose. Further, it incorporates all major constraints for robot manipulation: kinematic reachability, self-collision, and collision with obstacles. To solve the POMDP, we apply DESPOT, a state-of-the-art online POMDP algorithm. Our approach leverages two key ideas for computational efficiency. First, it performs lazy construction of a configuration-space lattice by interleaving construction of the lattice and online POMDP planning. Second, it combines online and offline POMDP planning by solving relaxed POMDP offline and using the solution to guide the online search algorithm. We evaluated the proposed approach on a seven degree-of-freedom robot arm in simulation environments. It significantly outperforms several existing algorithms, including some commonly used heuristics for contact manipulation under uncertainty.

Matthew Klingensmith, Michael C. Koval, Siddhartha S. Srinivasa et al.

We estimate the state a noisy robot arm and underactuated hand using an Implicit Manifold Particle Filter (MPF) informed by touch sensors. As the robot touches the world, its state space collapses to a contact manifold that we represent implicitly using a signed distance field. This allows us to extend the MPF to higher (six or more) dimensional state spaces. Earlier work (which explicitly represents the contact manifold) only shows the MPF in two or three dimensions. Through a series of experiments, we show that the implicit MPF converges faster and is more accurate than a conventional particle filter during periods of persistent contact. We present three methods of sampling the implicit contact manifold, and compare them in experiments.

Shervin Javdani, Matthew Klingensmith, J. Andrew Bagnell et al.

Many robotic systems deal with uncertainty by performing a sequence of information gathering actions. In this work, we focus on the problem of efficiently constructing such a sequence by drawing an explicit connection to submodularity. Ideally, we would like a method that finds the optimal sequence, taking the minimum amount of time while providing sufficient information. Finding this sequence, however, is generally intractable. As a result, many well-established methods select actions greedily. Surprisingly, this often performs well. Our work first explains this high performance -- we note a commonly used metric, reduction of Shannon entropy, is submodular under certain assumptions, rendering the greedy solution comparable to the optimal plan in the offline setting. However, reacting online to observations can increase performance. Recently developed notions of adaptive submodularity provide guarantees for a greedy algorithm in this online setting. In this work, we develop new methods based on adaptive submodularity for selecting a sequence of information gathering actions online. In addition to providing guarantees, we can capitalize on submodularity to attain additional computational speedups. We demonstrate the effectiveness of these methods in simulation and on a robot.