48 citations · 237 across the 29 of their papers we have counts for
27 papers · 1 filter
Dexterity from Smart Lenses: Multi-Fingered Robot Manipulation with In-the-Wild Human Demonstrations
Irmak Guzey, Haozhi Qi, Julen Urain +10
Learning multi-fingered robot policies from humans performing daily tasks in natural environments has long been a grand goal in the robotics community. Achieving this would mark si…
ACE: Adversarial Correspondence Embedding for Cross Morphology Motion Retargeting from Human to Nonhuman Characters
Tianyu Li, Jungdam Won, Alexander Clegg +3
Motion retargeting is a promising approach for generating natural and compelling animations for nonhuman characters. However, it is challenging to translate human movements into se…
ASC: Adaptive Skill Coordination for Robotic Mobile Manipulation
Naoki Yokoyama, Alex Clegg, Joanne Truong +6
We present Adaptive Skill Coordination (ASC) -- an approach for accomplishing long-horizon tasks like mobile pick-and-place (i.e., navigating to an object, picking it, navigating t…
Transformers are Adaptable Task Planners
Vidhi Jain, Yixin Lin, Eric Undersander +2
Every home is different, and every person likes things done in their particular way. Therefore, home robots of the future need to both reason about the sequential nature of day-to-…
Rethinking Sim2Real: Lower Fidelity Simulation Leads to Higher Sim2Real Transfer in Navigation
Joanne Truong, Max Rudolph, Naoki Yokoyama +3
If we want to train robots in simulation before deploying them in reality, it seems natural and almost self-evident to presume that reducing the sim2real gap involves creating simu…
Collaborative Navigation and Manipulation of a Cable-towed Load by Multiple Quadrupedal Robots
Chenyu Yang, Guo Ning Sue, Zhongyu Li +6
This paper tackles the problem of robots collaboratively towing a load with cables to a specified goal location while avoiding collisions in real time. The introduction of cables (…
Learning Torque Control for Quadrupedal Locomotion
Shuxiao Chen, Bike Zhang, Mark W. Mueller +2
Reinforcement learning (RL) has become a promising approach to developing controllers for quadrupedal robots. Conventionally, an RL design for locomotion follows a position-based p…
Vision-aided Dynamic Quadrupedal Locomotion on Discrete Terrain using Motion Libraries
Ayush Agrawal, Shuxiao Chen, Akshara Rai +1
In this paper, we present a framework rooted in control and planning that enables quadrupedal robots to traverse challenging terrains with discrete footholds using visual feedback.…
Learning-based Initialization Strategy for Safety of Multi-Vehicle Systems
Jennifer C. Shih, Akshara Rai, Laurent El Ghaoui
Multi-vehicle collision avoidance is a highly crucial problem due to the soaring interests of introducing autonomous vehicles into the real world in recent years. The safety of the…
Learning Periodic Tasks from Human Demonstrations
Jingyun Yang, Junwu Zhang, Connor Settle +3
We develop a method for learning periodic tasks from visual demonstrations. The core idea is to leverage periodicity in the policy structure to model periodic aspects of the tasks.…
FastMimic: Model-based Motion Imitation for Agile, Diverse and Generalizable Quadrupedal Locomotion
Tianyu Li, Jungdam Won, Sehoon Ha +1
Robots operating in human environments need various skills, like slow and fast walking, turning, side-stepping, and many more. However, building robot controllers that can exhibit…
Efficient and Interpretable Robot Manipulation with Graph Neural Networks
Yixin Lin, Austin S. Wang, Eric Undersander +1
Manipulation tasks, like loading a dishwasher, can be seen as a sequence of spatial constraints and relationships between different objects. We aim to discover these rules from dem…
Leveraging Forward Model Prediction Error for Learning Control
Sarah Bechtle, Bilal Hammoud, Akshara Rai +2
Learning for model based control can be sample-efficient and generalize well, however successfully learning models and controllers that represent the problem at hand can be challen…
Learning Navigation Skills for Legged Robots with Learned Robot Embeddings
Joanne Truong, Denis Yarats, Tianyu Li +4
Recent work has shown results on learning navigation policies for idealized cylinder agents in simulation and transferring them to real wheeled robots. Deploying such navigation po…
Model-Based Inverse Reinforcement Learning from Visual Demonstrations
Neha Das, Sarah Bechtle, Todor Davchev +3
Scaling model-based inverse reinforcement learning (IRL) to real robotic manipulation tasks with unknown dynamics remains an open problem. The key challenges lie in learning good d…
Planning in Learned Latent Action Spaces for Generalizable Legged Locomotion
Tianyu Li, Roberto Calandra, Deepak Pathak +3
Hierarchical learning has been successful at learning generalizable locomotion skills on walking robots in a sample-efficient manner. However, the low-dimensional "latent" action u…
Learning State-Dependent Losses for Inverse Dynamics Learning
Kristen Morse, Neha Das, Yixin Lin +3
Being able to quickly adapt to changes in dynamics is paramount in model-based control for object manipulation tasks. In order to influence fast adaptation of the inverse dynamics…
Encoding Physical Constraints in Differentiable Newton-Euler Algorithm
Giovanni Sutanto, Austin S. Wang, Yixin Lin +4
The recursive Newton-Euler Algorithm (RNEA) is a popular technique for computing the dynamics of robots. RNEA can be framed as a differentiable computational graph, enabling the dy…
Learning Generalizable Locomotion Skills with Hierarchical Reinforcement Learning
Tianyu Li, Nathan Lambert, Roberto Calandra +2
Learning to locomote to arbitrary goals on hardware remains a challenging problem for reinforcement learning. In this paper, we present a hierarchical learning framework that impro…
Bayesian Optimization in Variational Latent Spaces with Dynamic Compression
Rika Antonova, Akshara Rai, Tianyu Li +1
Data-efficiency is crucial for autonomous robots to adapt to new tasks and environments. In this work we focus on robotics problems with a budget of only 10-20 trials. This is a ve…
Curious iLQR: Resolving Uncertainty in Model-based RL
Sarah Bechtle, Yixin Lin, Akshara Rai +2
Curiosity as a means to explore during reinforcement learning problems has recently become very popular. However, very little progress has been made in utilizing curiosity for lear…
Using Deep Reinforcement Learning to Learn High-Level Policies on the ATRIAS Biped
Tianyu Li, Akshara Rai, Hartmut Geyer +1
Learning controllers for bipedal robots is a challenging problem, often requiring expert knowledge and extensive tuning of parameters that vary in different situations. Recently, d…
Using Simulation to Improve Sample-Efficiency of Bayesian Optimization for Bipedal Robots
Akshara Rai, Rika Antonova, Franziska Meier +1
Learning for control can acquire controllers for novel robotic tasks, paving the path for autonomous agents. Such controllers can be expert-designed policies, which typically requi…
Bayesian Optimization Using Domain Knowledge on the ATRIAS Biped
Akshara Rai, Rika Antonova, Seungmoon Song +3
Controllers in robotics often consist of expert-designed heuristics, which can be hard to tune in higher dimensions. It is typical to use simulation to learn these parameters, but…