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A User's Guide to Calibrating Robotics Simulators
Bhairav Mehta, Ankur Handa, Dieter Fox +1
Simulators are a critical component of modern robotics research. Strategies for both perception and decision making can be studied in simulation first before deployed to real world…
Anticipatory Navigation in Crowds by Probabilistic Prediction of Pedestrian Future Movements
Weiming Zhi, Tin Lai, Lionel Ott +1
Critical for the coexistence of humans and robots in dynamic environments is the capability for agents to understand each other's actions, and anticipate their movements. This pape…
STReSSD: Sim-To-Real from Sound for Stochastic Dynamics
Carolyn Matl, Yashraj Narang, Dieter Fox +2
Sound is an information-rich medium that captures dynamic physical events. This work presents STReSSD, a framework that uses sound to bridge the simulation-to-reality gap for stoch…
Fast Uncertainty Quantification for Deep Object Pose Estimation
Guanya Shi, Yifeng Zhu, Jonathan Tremblay +4
Deep learning-based object pose estimators are often unreliable and overconfident especially when the input image is outside the training domain, for instance, with sim2real transf…
Stein Variational Model Predictive Control
Alexander Lambert, Adam Fishman, Dieter Fox +2
Decision making under uncertainty is critical to real-world, autonomous systems. Model Predictive Control (MPC) methods have demonstrated favorable performance in practice, but rem…
Learning to Plan Optimally with Flow-based Motion Planner
Tin Lai, Fabio Ramos
Sampling-based motion planning is the predominant paradigm in many real-world robotic applications, but its performance is immensely dependent on the quality of the samples. The ma…
Multi-Objective Bayesian Optimisation and Joint Inversion for Active Sensor Fusion
Sebastian Haan, Fabio Ramos, Dietmar Müller
A critical decision process in data acquisition for mineral and energy resource exploration is how to efficiently combine a variety of sensor types and to minimize total cost. We p…
Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning
Anthony Tompkins, Rafael Oliveira, Fabio Ramos
We establish a general form of explicit, input-dependent, measure-valued warpings for learning nonstationary kernels. While stationary kernels are ubiquitous and simple to use, the…
Heteroscedastic Bayesian Optimisation for Stochastic Model Predictive Control
Rel Guzman, Rafael Oliveira, Fabio Ramos
Model predictive control (MPC) has been successful in applications involving the control of complex physical systems. This class of controllers leverages the information provided b…
Online Domain Adaptation for Occupancy Mapping
Anthony Tompkins, Ransalu Senanayake, Fabio Ramos
Creating accurate spatial representations that take into account uncertainty is critical for autonomous robots to safely navigate in unstructured environments. Although recent LIDA…
Euclideanizing Flows: Diffeomorphic Reduction for Learning Stable Dynamical Systems
Muhammad Asif Rana, Anqi Li, Dieter Fox +3
Robotic tasks often require motions with complex geometric structures. We present an approach to learn such motions from a limited number of human demonstrations by exploiting the…
Estimating Motion Uncertainty with Bayesian ICP
Fahira Afzal Maken, Fabio Ramos, Lionel Ott
Accurate uncertainty estimation associated with the pose transformation between two 3D point clouds is critical for autonomous navigation, grasping, and data fusion. Iterative clos…
Intrinsic Exploration as Multi-Objective RL
Philippe Morere, Fabio Ramos
Intrinsic motivation enables reinforcement learning (RL) agents to explore when rewards are very sparse, where traditional exploration heuristics such as Boltzmann or e-greedy woul…