Search Results for author:
Found 34 papers, 19 papers with code
Noise2Noise Denoising of CRISM Hyperspectral Data
Hyperspectral data acquired by the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) have allowed for unparalleled mapping of the surface mineralogy of Mars.
Fourier Transporter: Bi-Equivariant Robotic Manipulation in 3D
Many complex robotic manipulation tasks can be decomposed as a sequence of pick and place actions.
Leveraging Symmetries in Pick and Place
This paper analytically studies the symmetries present in planar robotic pick and place and proposes a method of incorporating equivariant neural models into Transporter Net in a way that captures all symmetries.
One-shot Imitation Learning via Interaction Warping
We propose a new method, Interaction Warping, for learning SE(3) robotic manipulation policies from a single demonstration.
On Robot Grasp Learning Using Equivariant Models
Real-world grasp detection is challenging due to the stochasticity in grasp dynamics and the noise in hardware.
Image to Sphere: Learning Equivariant Features for Efficient Pose Prediction
Predicting the pose of objects from a single image is an important but difficult computer vision problem.
The Surprising Effectiveness of Equivariant Models in Domains with Latent Symmetry
Extensive work has demonstrated that equivariant neural networks can significantly improve sample efficiency and generalization by enforcing an inductive bias in the network architecture.
Leveraging Fully Observable Policies for Learning under Partial Observability
Reinforcement learning in partially observable domains is challenging due to the lack of observable state information.
Partially Observable Reinforcement Learning
reinforcement-learning
+1
Edge Grasp Network: A Graph-Based SE(3)-invariant Approach to Grasp Detection
Given point cloud input, the problem of 6-DoF grasp pose detection is to identify a set of hand poses in SE(3) from which an object can be successfully grasped.
Image to Icosahedral Projection for $\mathrm{SO}(3)$ Object Reasoning from Single-View Images
In this paper, we propose a novel architecture based on icosahedral group convolutions that reasons in $\mathrm{SO(3)}$ by learning a projection of the input image onto an icosahedron.
Visual Foresight With a Local Dynamics Model
By combining the LDM with model-free policy learning, we can learn policies which can solve complex manipulation tasks using one-step lookahead planning.
Deep Transformer Q-Networks for Partially Observable Reinforcement Learning
Such tasks typically require some form of memory, where the agent has access to multiple past observations, in order to perform well.
Partially Observable Reinforcement Learning
reinforcement-learning
+1
Binding Actions to Objects in World Models
We study the problem of binding actions to objects in object-factored world models using action-attention mechanisms.
Factored World Models for Zero-Shot Generalization in Robotic Manipulation
In this paper, we learn to generalize over robotic pick-and-place tasks using object-factored world models, which combat the combinatorial explosion by ensuring that predictions are equivariant to permutations of objects.
GASCN: Graph Attention Shape Completion Network
Shape completion, the problem of inferring the complete geometry of an object given a partial point cloud, is an important problem in robotics and computer vision.
Equivariant Grasp learning In Real Time
Visual grasp detection is a key problem in robotics where the agent must learn to model the grasp function, a mapping from an image of a scene onto a set of feasible grasp poses.
Action Priors for Large Action Spaces in Robotics
This paper proposes an alternative approach where the solutions of previously solved tasks are used to produce an action prior that can facilitate exploration in future tasks.
Belief-Grounded Networks for Accelerated Robot Learning under Partial Observability
Many important robotics problems are partially observable in the sense that a single visual or force-feedback measurement is insufficient to reconstruct the state.
Robotic Pick-and-Place With Uncertain Object Instance Segmentation and Shape Completion
One approach is (a) use object instance segmentation and shape completion to model the objects and (b) use a regrasp planner to decide grasps and places displacing the models to their goals.
Robotics
Learning visual servo policies via planner cloning
Learning control policies for visual servoing in novel environments is an important problem.
Learning Discrete State Abstractions With Deep Variational Inference
In this work, we propose an information bottleneck method for learning approximate bisimulations, a type of state abstraction.
Learning Manipulation Skills Via Hierarchical Spatial Attention
Learning generalizable skills in robotic manipulation has long been challenging due to real-world sized observation and action spaces.
Robotics
Online Abstraction with MDP Homomorphisms for Deep Learning
Abstraction of Markov Decision Processes is a useful tool for solving complex problems, as it can ignore unimportant aspects of an environment, simplifying the process of learning an optimal policy.
AutOTranS: an Autonomous Open World Transportation System
Tasks in outdoor open world environments are now ripe for automation with mobile manipulators.
Robotics
Exploiting Environmental Variation to Improve Policy Robustness in Reinforcement Learning
We propose a novel method to exploit this observation to develop robust actor policies, by automatically developing a sampling curriculum over environment settings to use in training.
Adapting control policies from simulation to reality using a pairwise loss
This paper proposes an approach to domain transfer based on a pairwise loss function that helps transfer control policies learned in simulation onto a real robot.
Learning 6-DoF Grasping and Pick-Place Using Attention Focus
We address a class of manipulation problems where the robot perceives the scene with a depth sensor and can move its end effector in a space with six degrees of freedom -- 3D position and orientation.
Robotics
Hierarchical Reinforcement Learning with Hindsight
Reinforcement Learning (RL) algorithms can suffer from poor sample efficiency when rewards are delayed and sparse.
Hierarchical Reinforcement Learning
reinforcement-learning
+1
Learning Multi-Level Hierarchies with Hindsight
Hierarchical agents have the potential to solve sequential decision making tasks with greater sample efficiency than their non-hierarchical counterparts because hierarchical agents can break down tasks into sets of subtasks that only require short sequences of decisions.
Pick and Place Without Geometric Object Models
Whereas most deep RL approaches to robotic manipulation frame the problem in terms of low level states and actions, we propose a more abstract formulation.
Robotics
Grasp Pose Detection in Point Clouds
Many grasp detection methods achieve grasp success rates (grasp successes as a fraction of the total number of grasp attempts) between 75% and 95% for novel objects presented in isolation or in light clutter.
Robotics
Learning a visuomotor controller for real world robotic grasping using simulated depth images
This paper proposes an approach to learning a closed-loop controller for robotic grasping that dynamically guides the gripper to the object.
High precision grasp pose detection in dense clutter
Our focus in this paper is on improving the second step by using depth sensor scans from large online datasets to train a convolutional neural network.
Robotics
Using Geometry to Detect Grasps in 3D Point Clouds
Overall, our method achieves an average grasp success rate of 88% when grasping novels objects presented in isolation and an average success rate of 73% when grasping novel objects presented in dense clutter.
Robotics
