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Found 9 papers, 4 papers with code
DiffSim2Real: Deploying Quadrupedal Locomotion Policies Purely Trained in Differentiable Simulation
A key factor in our success is a smooth contact model that combines informative gradients with physical accuracy, ensuring effective transfer of learned behaviors.
PRIMER: Perception-Aware Robust Learning-based Multiagent Trajectory Planner
In decentralized multiagent trajectory planners, agents need to communicate and exchange their positions to generate collision-free trajectories.
RAYEN: Imposition of Hard Convex Constraints on Neural Networks
This paper presents RAYEN, a framework to impose hard convex constraints on the output or latent variable of a neural network.
NeBula: Quest for Robotic Autonomy in Challenging Environments; TEAM CoSTAR at the DARPA Subterranean Challenge
This paper presents and discusses algorithms, hardware, and software architecture developed by the TEAM CoSTAR (Collaborative SubTerranean Autonomous Robots), competing in the DARPA Subterranean Challenge.
Autonomous Off-road Navigation over Extreme Terrains with Perceptually-challenging Conditions
We propose a framework for resilient autonomous navigation in perceptually challenging unknown environments with mobility-stressing elements such as uneven surfaces with rocks and boulders, steep slopes, negative obstacles like cliffs and holes, and narrow passages.
FASTER: Fast and Safe Trajectory Planner for Navigation in Unknown Environments
The standard approaches that ensure safety by enforcing a "stop" condition in the free-known space can severely limit the speed of the vehicle, especially in situations where much of the world is unknown.
Personalized Cancer Chemotherapy Schedule: a numerical comparison of performance and robustness in model-based and model-free scheduling methodologies
Reinforcement learning algorithms are gaining popularity in fields in which optimal scheduling is important, and oncology is not an exception.
FaSTraP: Fast and Safe Trajectory Planner for Flights in Unknown Environments
The desire of maintaining computational tractability typically leads to optimization problems that do not include the obstacles (collision checks are done on the solutions) or to formulations that use a convex decomposition of the free space and then impose an ad hoc allocation of each interval of the trajectory in a specific polyhedron.
Robotics
Real-Time Planning with Multi-Fidelity Models for Agile Flights in Unknown Environments
However, in unknown environments, this approach can lead to erratic or unstable behavior due to the interaction between the global planner, whose solution is changing constantly, and the local planner; a consequence of not capturing higher-order dynamics in the global plan.
Robotics
