Being able to reproduce physical phenomena ranging from light interaction to contact mechanics, simulators are becoming increasingly useful in more and more application domains where real-world interaction or labeled data are difficult to obtain.
Language-guided Embodied AI benchmarks requiring an agent to navigate an environment and manipulate objects typically allow one-way communication: the human user gives a natural language command to the agent, and the agent can only follow the command passively.
We introduce a novel privacy-preserving methodology for performing Visual Question Answering on the edge.
Learning complex manipulation tasks in realistic, obstructed environments is a challenging problem due to hard exploration in the presence of obstacles and high-dimensional visual observations.
However, current simulators for Embodied AI (EAI) challenges only provide simulated indoor scenes with a limited number of layouts.
We take a fresh look at this problem, by considering a setting in which the robot is limited to storing that knowledge and experience only in the form of learned skill policies.
In this survey article, we analyze how resilience is achieved in networks of agents and multi-robot systems that are able to overcome adversity by leveraging system-wide complementarity, diversity, and redundancy - often involving a reconfiguration of robotic capabilities to provide some key ability that was not present in the system a priori.
We find that by using knowledge of the number of rollouts allocated, the agent can more effectively choose actions to explore.
We address the latter problem of estimating parameters through a Bayesian inference approach that approximates a posterior distribution over simulation parameters given real sensor measurements.
Differentiable simulators provide an avenue for closing the sim-to-real gap by enabling the use of efficient, gradient-based optimization algorithms to find the simulation parameters that best fit the observed sensor readings.
In contrast, motion planners use explicit models of the agent and environment to plan collision-free paths to faraway goals, but suffer from inaccurate models in tasks that require contacts with the environment.
Constrained robot motion planning is a widely used technique to solve complex robot tasks.
We present a differentiable simulation architecture for articulated rigid-body dynamics that enables the augmentation of analytical models with neural networks at any point of the computation.
Motion planning with constraints is an important part of many real-world robotic systems.
Robotics Computational Geometry
One of the great promises of robot learning systems is that they will be able to learn from their mistakes and continuously adapt to ever-changing environments, but most robot learning systems today are deployed as fixed policies which do not adapt after deployment.
We address the problem of planning robot motions in constrained configuration spaces where the constraints change throughout the motion.
Robotics Computational Geometry
We show how these embedding spaces can then be used as an augmentation to the robot state in reinforcement learning problems.
One of the great promises of robot learning systems is that they will be able to learn from their mistakes and continuously adapt to ever-changing environments.
Planning smooth and energy-efficient motions for wheeled mobile robots is a central task for applications ranging from autonomous driving to service and intralogistic robotics.
The recursive Newton-Euler Algorithm (RNEA) is a popular technique for computing the dynamics of robots.
A key ingredient to achieving intelligent behavior is physical understanding that equips robots with the ability to reason about the effects of their actions in a dynamic environment.
Light Detection and Ranging (LIDAR) sensors play an important role in the perception stack of autonomous robots, supplying mapping and localization pipelines with depth measurements of the environment.
We propose a centralized control framework to select suitable robots from a heterogeneous pool and place them at appropriate locations to monitor a region for events of interest.
While learning-based models of the environment dynamics have contributed to significant improvements in sample efficiency compared to model-free reinforcement learning algorithms, they typically fail to generalize to system states beyond the training data, while often grounding their predictions on non-interpretable latent variables.
Quadrotor stabilizing controllers often require careful, model-specific tuning for safe operation.
Personal robots assisting humans must perform complex manipulation tasks that are typically difficult to specify in traditional motion planning pipelines, where multiple objectives must be met and the high-level context be taken into consideration.
In order to deal with complex terrain observations and policy learning, we propose a value iteration recurrence, referred to as the soft value iteration network (SVIN).
Different from most existing works where the profits are assumed to be static, in this work we investigate a variant that has arbitrary time-dependent profits.
A big challenge in environmental monitoring is the spatiotemporal variation of the phenomena to be observed.