96 citations · 456 across the 88 of their papers we have counts for
11 papers · 1 filter
IKFlow: Generating Diverse Inverse Kinematics Solutions
Barrett Ames, Jeremy Morgan, George Konidaris
Inverse kinematics - finding joint poses that reach a given Cartesian-space end-effector pose - is a common operation in robotics, since goals and waypoints are typically defined i…
Guided Policy Search for Parameterized Skills using Adverbs
Benjamin A. Spiegel, George Konidaris
We present a method for using adverb phrases to adjust skill parameters via learned adverb-skill groundings. These groundings allow an agent to use adverb feedback provided by a hu…
Coarse-Grained Smoothness for RL in Metric Spaces
Omer Gottesman, Kavosh Asadi, Cameron Allen +3
Principled decision-making in continuous state--action spaces is impossible without some assumptions. A common approach is to assume Lipschitz continuity of the Q-function. We show…
Learning to Infer Kinematic Hierarchies for Novel Object Instances
Hameed Abdul-Rashid, Miles Freeman, Ben Abbatematteo +2
Manipulating an articulated object requires perceiving itskinematic hierarchy: its parts, how each can move, and howthose motions are coupled. Previous work has explored per-ceptio…
Towards Optimal Correlational Object Search
Kaiyu Zheng, Rohan Chitnis, Yoonchang Sung +2
In realistic applications of object search, robots will need to locate target objects in complex environments while coping with unreliable sensors, especially for small or hard-to-…
Generalizing to New Domains by Mapping Natural Language to Lifted LTL
Eric Hsiung, Hiloni Mehta, Junchi Chu +4
Recent work on using natural language to specify commands to robots has grounded that language to LTL. However, mapping natural language task specifications to LTL task specificati…
RMPs for Safe Impedance Control in Contact-Rich Manipulation
Seiji Shaw, Ben Abbatematteo, George Konidaris
Variable impedance control in operation-space is a promising approach to learning contact-rich manipulation behaviors. One of the main challenges with this approach is producing a…
HAC Explore: Accelerating Exploration with Hierarchical Reinforcement Learning
Willie McClinton, Andrew Levy, George Konidaris
Sparse rewards and long time horizons remain challenging for reinforcement learning algorithms. Exploration bonuses can help in sparse reward settings by encouraging agents to expl…
Value-Based Reinforcement Learning for Continuous Control Robotic Manipulation in Multi-Task Sparse Reward Settings
Sreehari Rammohan, Shangqun Yu, Bowen He +4
Learning continuous control in high-dimensional sparse reward settings, such as robotic manipulation, is a challenging problem due to the number of samples often required to obtain…
Learning to Detect Multi-Modal Grasps for Dexterous Grasping in Dense Clutter
Matt Corsaro, Stefanie Tellex, George Konidaris
We propose an approach to multi-modal grasp detection that jointly predicts the probabilities that several types of grasps succeed at a given grasp pose. Given a partial point clou…
Bootstrapping Motor Skill Learning with Motion Planning
Ben Abbatematteo, Eric Rosen, Stefanie Tellex +1
Learning a robot motor skill from scratch is impractically slow; so much so that in practice, learning must be bootstrapped using a good skill policy obtained from human demonstrat…