Faria, Miguel (INESC-ID and Instituto Superior Técnico, Universidade de Lisboa) | Silva, Rui (INESC-ID and Instituto Superior Técnico, Universidade de Lisboa) | Melo, Francisco S. (INESC-ID and Instituto Superior Técnico, Universidade de Lisboa) | Paiva, Ana (INESC-ID and Instituto Superior Técnico, Universidade de Lisboa)
This paper presents an ongoing study in the area of Human-Robot Collaboration, more precisely collaborative manipulation tasks between one robot and multiple people. We study how different trajectories influence people's perception of the robot's goal. To achieve this, we propose an approach based on Probabilistic Motor Primitives and the notion of legibility and predictability of trajectories to create the movements the robot performs during task execution. In this approach we also propose combining of legible and predictable trajectories depending on the state of the task in order to diminish the drawbacks associated with each type of trajectory.
Robots that deal with fast-moving objects tend to handle them in one of two ways: way one is to assume that the object is going to keep doing whatever it's been doing, allowing you to predict what's going to happen with it without having to work too hard. Way two is to instead constantly watch what the object is doing, and then continually update what's going to happen to it by working very hard. Way one is unreliable because the Universe is unreliable and assumptions are dangerous, and way two is very computationally intensive, which often makes it too slow to feed useful instructions through a controller to a robot. At the Learning Algorithms and Systems Laboratory at EPFL, they're leveraging fast vision, fast computers, fast controllers, fast motors, programming by demonstration, and object modeling to be able to snatch unpredictably unbalanced flying objects straight out of the air. The most impressive thing here is that the robot is able to catch objects that are both statically and dynamically unbalanced.
Researchers at Rice University have developed a way to train robots with just a little push. Their method uses algorithms that allow robots to not only respond to a human's touch in the moment, but alter their trajectory based on that physical input. "Here the robot has a plan, or desired trajectory, which describes how the robot thinks it should perform the task," said graduate student Dylan Losey about the project. "We introduced a real-time algorithm that modified, or deformed, the robot's future desired trajectory."
A major challenge for future social robots is the high-level interpretation of human motion, and the consequent generation of appropriate robot actions. This paper describes some fundamental steps towards the real-time implementation of a system that allows a mobile robot to transform quantitative information about human trajectories (i.e. coordinates and speed) into qualitative concepts, and from these to generate appropriate control commands. The problem is formulated using a simple version of qualitative trajectory calculus, then solved using an inference engine based on fuzzy temporal logic and situation graph trees. Preliminary results are discussed and future directions of the current research are drawn.
Evolutionary algorithms have demonstrated excellent results for many engineering optimization problems. In other way, recently, the chaos theory concepts and chaotic times series have gained much attention during this decade for the design of stochastic search algorithms. Differential evolution is a new evolutionary algorithm mainly having three advantages: finds the global minimum regardless of the initial parameter values, fast convergence and uses few control parameters. In this work, a new hybrid approach of Differential Evolution combined with Chaos (DEC) is presented for the optimization for path planning of mobile robots. The new chaotic operators are based on logistic map with exponential and cosinoidal decreasing.