Gaussian-Process-based Robot Learning from Demonstration

Endowed with higher levels of autonomy, robots are required to perform increasingly complex manipulation tasks. Learning from demonstration is arising as a promising paradigm for transferring skills to robots. It allows to implicitly learn task constraints from observing the motion executed by a human teacher, which can enable adaptive behavior. We present a novel Gaussian-Process-based learning from demonstration approach. This probabilistic representation allows to generalize over multiple demonstrations, and encode variability along the different phases of the task. In this paper, we address how Gaussian Processes can be used to effectively learn a policy from trajectories in task space. We also present a method to efficiently adapt the policy to fulfill new requirements, and to modulate the robot behavior as a function of task variability. This approach is illustrated through a real-world application using the TIAGo robot.Comment: 8 pages, 10 figure

A multi-scale approach to the physics of ion beam cancer therapy

We propose a multi-scale approach to understanding physics related to the ion/proton-beam cancer therapy and calculation of the probability of the DNA damage as a result of irradiation of patients with energetic (up to 430 MeV/u) ions. This approach is inclusive with respect to different scales starting from the long scale defined by the ion stopping followed by a smaller scale defined by secondary electrons and radicals ending with the shortest scale defined by interactions of secondaries with the DNA. We present calculations of the probabilities of single and double strand breaks of the DNA and suggest a way of further elaboration of such calculations.Comment: submitted to RADAM2008 proceedings. 8 pages,5 Figures, class files for AIP include

Physics of ion beam cancer therapy: a multi-scale approach

We propose a multi-scale approach to understand the physics related to ion-beam cancer therapy. It allows the calculation of the probability of DNA damage as a result of irradiation of tissues with energetic ions, up to 430 MeV/u. This approach covers different scales, starting from the large scale, defined by the ion stopping, followed by a smaller scale, defined by secondary electrons and radicals, and ending with the shortest scale, defined by interactions of secondaries with the DNA. We present calculations of the probabilities of single and double strand breaks of DNA, suggest a way to further expand such calculations, and also make some estimates for glial cells exposed to radiation.Comment: 18 pag,5 fig, submitted to PR

Learning to Prevent Monocular SLAM Failure using Reinforcement Learning

Monocular SLAM refers to using a single camera to estimate robot ego motion while building a map of the environment. While Monocular SLAM is a well studied problem, automating Monocular SLAM by integrating it with trajectory planning frameworks is particularly challenging. This paper presents a novel formulation based on Reinforcement Learning (RL) that generates fail safe trajectories wherein the SLAM generated outputs do not deviate largely from their true values. Quintessentially, the RL framework successfully learns the otherwise complex relation between perceptual inputs and motor actions and uses this knowledge to generate trajectories that do not cause failure of SLAM. We show systematically in simulations how the quality of the SLAM dramatically improves when trajectories are computed using RL. Our method scales effectively across Monocular SLAM frameworks in both simulation and in real world experiments with a mobile robot.Comment: Accepted at the 11th Indian Conference on Computer Vision, Graphics and Image Processing (ICVGIP) 2018 More info can be found at the project page at https://robotics.iiit.ac.in/people/vignesh.prasad/SLAMSafePlanner.html and the supplementary video can be found at https://www.youtube.com/watch?v=420QmM_Z8v

Measurements of the neutron electric to magnetic form factor ratio G_(En)/G_(Mn) via the ^2H(e, e'n)^1H reaction to Q^2 = 1.45 (GeV/c)^2

We report values for the neutron electric tomagnetic form factor ratio,G_(En)/G_(Mn), deduced frommeasurements of the neutron’s recoil polarization in the quasielastic ^2H(e, e'n)^1H reaction, at three Q^2 values of 0.45, 1.13, and 1.45 (GeV/c)^2. The data at Q^2 = 1.13 and 1.45 (GeV/c)^2 are the first direct experimental measurements of G_(En) employing polarization degrees of freedom in the Q^2 > 1 (GeV/c)^2 region and stand as the most precise determinations of G_(En) for all values of Q^2

Provably Safe Robot Navigation with Obstacle Uncertainty

As drones and autonomous cars become more widespread it is becoming increasingly important that robots can operate safely under realistic conditions. The noisy information fed into real systems means that robots must use estimates of the environment to plan navigation. Efficiently guaranteeing that the resulting motion plans are safe under these circumstances has proved difficult. We examine how to guarantee that a trajectory or policy is safe with only imperfect observations of the environment. We examine the implications of various mathematical formalisms of safety and arrive at a mathematical notion of safety of a long-term execution, even when conditioned on observational information. We present efficient algorithms that can prove that trajectories or policies are safe with much tighter bounds than in previous work. Notably, the complexity of the environment does not affect our methods ability to evaluate if a trajectory or policy is safe. We then use these safety checking methods to design a safe variant of the RRT planning algorithm.Comment: RSS 201