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Active Perception using Neural Radiance Fields
Oct 15, 2023
We study active perception from first principles to argue that an autonomous agent performing active perception should maximize the mutual information that past observations posses about future ones. Doing so requires (a) a representation of the scene that summarizes past observations and the ability to update this representation to incorporate new observations (state estimation and mapping), (b) the ability to synthesize new observations of the scene (a generative model), and (c) the ability to select control trajectories that maximize predictive information (planning). This motivates a neural radiance field (NeRF)-like representation which captures photometric, geometric and semantic properties of the scene grounded. This representation is well-suited to synthesizing new observations from different viewpoints. And thereby, a sampling-based planner can be used to calculate the predictive information from synthetic observations along dynamically-feasible trajectories. We use active perception for exploring cluttered indoor environments and employ a notion of semantic uncertainty to check for the successful completion of an exploration task. We demonstrate these ideas via simulation in realistic 3D indoor environments.
Design and Evaluation of Motion Planners for Quadrotors
Sep 24, 2023The field of quadrotor motion planning has experienced significant advancements over the last decade. Most successful approaches rely on two stages: a front-end that determines the best path by incorporating geometric (and in some cases kinematic or input) constraints, that effectively specify the homotopy class of the trajectory; and a back-end that optimizes the path with a suitable objective function, constrained by the robot's dynamics as well as state/input constraints. However, there is no systematic approach or design guidelines to design both the front and the back ends for a wide range of environments, and no literature evaluates the performance of the trajectory planning algorithm with varying degrees of environment complexity. In this paper, we propose a modular approach to designing the software planning stack and offer a parameterized set of environments to systematically evaluate the performance of two-stage planners. Our parametrized environments enable us to access different front and back-end planners as a function of environmental clutter and complexity. We use simulation and experimental results to demonstrate the performance of selected planning algorithms across a range of environments. Finally, we open source the planning/evaluation stack and parameterized environments to facilitate more in-depth studies of quadrotor motion planning, available at https://github.com/KumarRobotics/kr_mp_design
Reachability-based Trajectory Safeguard (RTS): A Safe and Fast Reinforcement Learning Safety Layer for Continuous Control
Nov 17, 2020
Reinforcement Learning (RL) algorithms have achieved remarkable performance in decision making and control tasks due to their ability to reason about long-term, cumulative reward using trial and error. However, during RL training, applying this trial-and-error approach to real-world robots operating in safety critical environment may lead to collisions. To address this challenge, this paper proposes a Reachability-based Trajectory Safeguard (RTS), which leverages trajectory parameterization and reachability analysis to ensure safety while a policy is being learned. This method ensures a robot with continuous action space can be trained from scratch safely in real-time. Importantly, this safety layer can still be applied after a policy has been learned. The efficacy of this method is illustrated on three nonlinear robot models, including a 12-D quadrotor drone, in simulation. By ensuring safety with RTS, this paper demonstrates that the proposed algorithm is not only safe, but can achieve a higher reward in a considerably shorter training time when compared to a non-safe counterpart.