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—Robots often have to perform manipulation tasks in close proximity to people (Fig 1). As such, it is desirable to use a robot arm that has limited joint torques so as to not injure the nearby person. Unfortunately, these limited torques then limit the payload capability of the arm. By using contact with the environment, robots can expand their reachable workspace that, otherwise, would be inaccessible due to exceeding actuator torque limits. We adapt our recently developed INSAT algorithm [1] to tackle the problem of torque-limited whole arm manipulation planning through contact. INSAT requires no prior over contact mode sequence and no initial template or seed for trajectory optimization. INSAT achieves this by interleaving graph search to explore the manipulator joint configuration space with incre- mental trajectory optimizations seeded by neighborhood solutions to find a dynamically feasible trajectory through contact. We demonstrate our results on a variety of manipulators and scenarios in simulation. We also experimentally show our planner exploiting robot-environment contact for the pick and place of a payload using a Kinova Gen3 robot. In comparison to the same trajectory running in free space, we experimentally show that the utilization of bracing contacts reduces the overall torque required to execute the trajectory. 

We consider a planning problem for a robot operating in an information-degraded environment. Our contribution to the state of the art is addressing this problem when robots have limited sensing capabilities, and thus only acquire information in certain locations. We therefore need a method that balances between driving the robot to the goal and toward regions to gain information (or to reduce uncertainty). We present a novel sampling-based planner (Particle Filter based Affine Quadratic Tree --- PF-AQT) that explores the environment, and plans to reach a goal with minimal uncertainty. We then use the output trajectory from PF-AQT to initialize an optimization-based planner that finds a locally optimal trajectory that minimizes control effort and uncertainty. In doing so we reap the exploration benefits of sampling-based methods and exploitation benefits of optimization-based methods for dealing with uncertainty and limited sensing capabilities of the robot. We demonstrate our results using two dynamical systems: double integrator model and a non-holonomic car-like robot. 

In the future, human-robot interaction will include collaboration in close-quarters where the environment geometry is partially unknown. As a means for enabling such interaction, this paper presents a multi-modal sensor array capable of contact detection and localization, force sensing, proximity sensing, and mapping. The sensor array integrates Hall effect and time-of-flight (ToF) sensors in an I2C communication network. The design, fabrication, and characterization of the sensor array for a future in-situ collaborative continuum robot are presented. Possible perception benefits of the sensor array are demonstrated for accidental contact detection, mapping of the environment, selection of admissible zones for bracing, and constrained motion control of the end effector while maintaining a bracing constraint with an admissible rolling motion.