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Found 20 papers, 13 papers with code
Decentralization and Acceleration Enables Large-Scale Bundle Adjustment
In this paper, we present a fully decentralized method that alleviates computation and communication bottlenecks to solve arbitrarily large bundle adjustment problems.
USA-Net: Unified Semantic and Affordance Representations for Robot Memory
In order for robots to follow open-ended instructions like "go open the brown cabinet over the sink", they require an understanding of both the scene geometry and the semantics of their environment.
Neural Contact Fields: Tracking Extrinsic Contact with Tactile Sensing
In experiments, we find that Neural Contact Fields are able to localize multiple contact patches without making any assumptions about the geometry of the contact, and capture contact/no-contact transitions for known categories of objects with unseen shapes in unseen environment configurations.
Theseus: A Library for Differentiable Nonlinear Optimization
We present Theseus, an efficient application-agnostic open source library for differentiable nonlinear least squares (DNLS) optimization built on PyTorch, providing a common framework for end-to-end structured learning in robotics and vision.
TASKOGRAPHY: Evaluating robot task planning over large 3D scene graphs
3D scene graphs (3DSGs) are an emerging description; unifying symbolic, topological, and metric scene representations.
iSDF: Real-Time Neural Signed Distance Fields for Robot Perception
We present iSDF, a continual learning system for real-time signed distance field (SDF) reconstruction.
A Differentiable Recipe for Learning Visual Non-Prehensile Planar Manipulation
Specifying tasks with videos is a powerful technique towards acquiring novel and general robot skills.
No RL, No Simulation: Learning to Navigate without Navigating
Most prior methods for learning navigation policies require access to simulation environments, as they need online policy interaction and rely on ground-truth maps for rewards.
Learning Complex Geometric Structures from Data with Deep Riemannian Manifolds
We present Deep Riemannian Manifolds, a new class of neural network parameterized Riemannian manifolds that can represent and learn complex geometric structures.
Habitat 2.0: Training Home Assistants to Rearrange their Habitat
We introduce Habitat 2. 0 (H2. 0), a simulation platform for training virtual robots in interactive 3D environments and complex physics-enabled scenarios.
Revitalizing Optimization for 3D Human Pose and Shape Estimation: A Sparse Constrained Formulation
We propose a novel sparse constrained formulation and from it derive a real-time optimization method for 3D human pose and shape estimation.
Where2Act: From Pixels to Actions for Articulated 3D Objects
One of the fundamental goals of visual perception is to allow agents to meaningfully interact with their environment.
Learning Tactile Models for Factor Graph-based Estimation
In order to incorporate tactile measurements in the graph, we need local observation models that can map high-dimensional tactile images onto a low-dimensional state space.
Neural Dynamic Policies for End-to-End Sensorimotor Learning
We show that NDPs outperform the prior state-of-the-art in terms of either efficiency or performance across several robotic control tasks for both imitation and reinforcement learning setups.
Ranked #4 on
Meta-Learning
on MT50
Batteries, camera, action! Learning a semantic control space for expressive robot cinematography
First, we generate a database of video clips with a diverse range of shots in a photo-realistic simulator, and use hundreds of participants in a crowd-sourcing framework to obtain scores for a set of semantic descriptors for each clip.
Encoding Physical Constraints in Differentiable Newton-Euler Algorithm
The recursive Newton-Euler Algorithm (RNEA) is a popular technique for computing the dynamics of robots.
Robotics
Riemannian Motion Policy Fusion through Learnable Lyapunov Function Reshaping
RMPfusion supplements RMPflow with weight functions that can hierarchically reshape the Lyapunov functions of the subtask RMPs according to the current configuration of the robot and environment.
Multi-Objective Policy Generation for Multi-Robot Systems Using Riemannian Motion Policies
We propose a collection of RMPs for simple multi-robot tasks that can be used for building controllers for more complicated tasks.
Robotics
RMPflow: A Computational Graph for Automatic Motion Policy Generation
We develop a novel policy synthesis algorithm, RMPflow, based on geometrically consistent transformations of Riemannian Motion Policies (RMPs).
Robotics Systems and Control
Continuous-Time Gaussian Process Motion Planning via Probabilistic Inference
We benchmark our algorithms against several sampling-based and trajectory optimization-based motion planning algorithms on planning problems in multiple environments.
Robotics
