Felipe Padula Sanches

The advent of collaborative industrial and household robotics has blurred the demarcation between the human and robot workspace. The capability of robots to function efficiently alongside humans requires new research to be conducted in dynamic environments as opposed to the traditional wellstructured laboratory. In this work, we propose an efficient skill transfer methodology comprising intuitive interfaces, efficient optical tracking systems, and compliant control of robotic armhand systems. The lightweight wearable interfaces mounted with robotic grippers and hands allow the execution of dexterous activities in dynamic environments without restricting human dexterity. The fiducial and reflective markers mounted on the interfaces facilitate the extraction of positional and rotational information allowing efficient trajectory tracking. As the tasks are performed using the mounted grippers and hands, gripper state information can be directly transferred. The hardware-agnostic nature and efficiency of the proposed interfaces and skill transfer methodology are demonstrated through the execution of complex tasks that require increased dexterity, writing and drawing.

To communicate, the  1.5 million Americans living with deafblindess use tactile American Sign Language (t-ASL). To provide DeafBlind (DB) individuals with a means of using their primary communication language without the use of an interpreter, we developed an assistive technology that promotes their autonomy. The TATUM (Tactile ASL Translational User Mechanism) anthropomorphic arm hand system leverages previous developments of a fingerspelling hand to sign more complex ASL words and phrases. The TATUM hand-wrist system is attached onto a 4 DOF robot arm and a human motion recognition and human to robot gesture transfer framework is used for signing recognition and replication. In particular, signing trajectories based on vision-based motion capture data from a sign demonstrator were used to control the robot’s actuators. The performance of the system was evaluated through tactile based sign recognition performed by a blinded user and for its accuracy with novice, sighted users.

Recent changes in industrial paradigms enforce that robots must be intelligent and capable of decision-making. Robotic manipulators need to satisfy many requirements for operating properly. Perhaps the most fundamental one is the capability of operating in its environment without collisions. In this work, we perform visual obstacle avoidance on goal-reaching tasks of a robotic manipulator using an end-to-end Deep Reinforcement Learning model. The motion control policy is responsible for reaching a target position while at the same time avoiding an obstacle positioned randomly in the scene. This policy uses vision and proprioceptive sensor data to operate. We train the reinforcement learning agent using Twin-Delayed DDPG (TD3) algorithm in a simulated environment, utilizing the Unity game engine and the ML-Agents toolkit. Experiments demonstrate that the agent can successfully learn a meaningful policy to avoid obstacles using images.