Search Results for author:
Found 23 papers, 17 papers with code
EvDNeRF: Reconstructing Event Data with Dynamic Neural Radiance Fields
We present EvDNeRF, a pipeline for generating event data and training an event-based dynamic NeRF, for the purpose of faithfully reconstructing eventstreams on scenes with rigid and non-rigid deformations that may be too fast to capture with a standard camera.
Geometric Clifford Algebra Networks
GCANs are based on symmetry group transformations using geometric (Clifford) algebras.
ClimaX: A foundation model for weather and climate
We develop and demonstrate ClimaX, a flexible and generalizable deep learning model for weather and climate science that can be trained using heterogeneous datasets spanning different variables, spatio-temporal coverage, and physical groundings.
Agent-Time Attention for Sparse Rewards Multi-Agent Reinforcement Learning
Sparse and delayed rewards pose a challenge to single agent reinforcement learning.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Learning Modular Simulations for Homogeneous Systems
We evaluate our framework on a variety of systems and show that message passing allows coordination between multiple modules over time for accurate predictions and in certain cases, enables zero-shot generalization to new system configurations.
Towards Multi-spatiotemporal-scale Generalized PDE Modeling
Finally, we show promising results on generalization to different PDE parameters and time-scales with a single surrogate model.
Learning to Simulate Realistic LiDARs
Simulating realistic sensors is a challenging part in data generation for autonomous systems, often involving carefully handcrafted sensor design, scene properties, and physics modeling.
Clifford Neural Layers for PDE Modeling
We empirically evaluate the benefit of Clifford neural layers by replacing convolution and Fourier operations in common neural PDE surrogates by their Clifford counterparts on 2D Navier-Stokes and weather modeling tasks, as well as 3D Maxwell equations.
Recursive Reasoning Graph for Multi-Agent Reinforcement Learning
Multi-agent reinforcement learning (MARL) provides an efficient way for simultaneously learning policies for multiple agents interacting with each other.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Training Structured Mechanical Models by Minimizing Discrete Euler-Lagrange Residual
Structured Mechanical Models (SMMs) are a data-efficient black-box parameterization of mechanical systems, typically fit to data by minimizing the error between predicted and observed accelerations or next states.
Scalable Anytime Planning for Multi-Agent MDPs
We also introduce a multi-drone delivery domain with dynamic, i. e., state-dependent coordination graphs, and demonstrate how our approach scales to large problems on this domain that are intractable for other MCTS methods.
Scalable Identification of Partially Observed Systems with Certainty-Equivalent EM
System identification is a key step for model-based control, estimator design, and output prediction.
Deep Implicit Coordination Graphs for Multi-agent Reinforcement Learning
Coordination graph based formalization allows reasoning about the joint action based on the structure of interactions.
Dynamic Multi-Robot Task Allocation under Uncertainty and Temporal Constraints
We consider the problem of dynamically allocating tasks to multiple agents under time window constraints and task completion uncertainty.
Structured Mechanical Models for Robot Learning and Control
Deep neural networks have been used to learn models of robot dynamics from data, but they suffer from data-inefficiency and the difficulty to incorporate prior knowledge.
Health-Informed Policy Gradients for Multi-Agent Reinforcement Learning
This paper proposes a definition of system health in the context of multiple agents optimizing a joint reward function.
Multi-agent Reinforcement Learning
Policy Gradient Methods
+2
Simulating Emergent Properties of Human Driving Behavior Using Multi-Agent Reward Augmented Imitation Learning
Recent developments in multi-agent imitation learning have shown promising results for modeling the behavior of human drivers.
Model Primitive Hierarchical Lifelong Reinforcement Learning
Learning interpretable and transferable subpolicies and performing task decomposition from a single, complex task is difficult.
A General Framework for Structured Learning of Mechanical Systems
We address the need for a flexible, gray-box model of mechanical systems that can seamlessly incorporate prior knowledge where it is available, and train expressive function approximators where it is not.
Model-based Reinforcement Learning
Reinforcement Learning (RL)
Learning Policy Representations in Multiagent Systems
Modeling agent behavior is central to understanding the emergence of complex phenomena in multiagent systems.
Layer-wise synapse optimization for implementing neural networks on general neuromorphic architectures
An optimal set of synapse weights may then be found for a given choice of ANN activation function and SNN neuron.
Model-Free Imitation Learning with Policy Optimization
In imitation learning, an agent learns how to behave in an environment with an unknown cost function by mimicking expert demonstrations.
PlanIt: A Crowdsourcing Approach for Learning to Plan Paths from Large Scale Preference Feedback
We represent trajectory preferences using a cost function that the robot learns and uses it to generate good trajectories in new environments.
