In this paper we present an approach to localize planar furniture parts in 3D range camera data for autonomous robot manipulation, that estimates both their six degree of freedom (DoF) poses and their dimensions. Range cameras are a promising sensor category for mobile robotics. Unfortunately, many of them come with a considerable measurement noise, that leads to difficulties when trying to detect objects or their parts e.g. using canonical methods for range image segmentation. In contrast, our approach is able to overcome these issues by combining concepts of 2D and 3D computer vision as well as integrating intensity and depth data on several levels of abstraction. Therefore it is not restricted to range sensors with high image quality and scales on cameras with lower image quality, too. This concept is generic and has been implemented for elliptical object parts as a proof of concept.
We present TrueRMA, a data-efficient, modelfree method to learn cost-optimized robot trajectories over a wide range of starting points and endpoints. The key idea is to calculate trajectory waypoints in Cartesian space by recursively predicting orthogonal adaptations relative to the midpoints of straight lines. We generate a differentiable path by adding circular blends around the waypoints, calculate the corresponding joint positions with an inverse kinematics solver and calculate a time-optimal parameterization considering velocity and acceleration limits. During training, the trajectory is executed in a physics simulator and costs are assigned according to a user-specified cost function which is not required to be differentiable. Given a starting point and an endpoint as input, a neural network is trained to predict midpoint adaptations that minimize the cost of the resulting trajectory via reinforcement learning. We successfully train a KUKA iiwa robot to keep a ball on a plate while moving between specified points and compare the performance of TrueRMA against two baselines. The results show that our method requires less training data to learn the task while generating shorter and faster trajectories.
We present TrueAdapt, a model-free method to learn online adaptations of robot trajectories based on their effects on the environment. Given sensory feedback and future waypoints of the original trajectory, a neural network is trained to predict joint accelerations at regular intervals. The adapted trajectory is generated by linear interpolation of the predicted accelerations, leading to continuously differentiable joint velocities and positions. Bounded jerks, accelerations and velocities are guaranteed by calculating the valid acceleration range at each decision step and clipping the network's output accordingly. A deviation penalty during the training process causes the adapted trajectory to follow the original one. Smooth movements are encouraged by penalizing high accelerations and jerks. We evaluate our approach by training a simulated KUKA iiwa robot to balance a ball on a plate while moving and demonstrate that the balancing policy can be directly transferred to a real robot with little impact on performance.
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