Search Results for author: Karl Pertsch

Found 18 papers, 4 papers with code

SPRINT: Scalable Policy Pre-Training via Language Instruction Relabeling

no code implementations20 Jun 2023 Jesse Zhang, Karl Pertsch, Jiahui Zhang, Joseph J. Lim

Pre-training robot policies with a rich set of skills can substantially accelerate the learning of downstream tasks.

Cross-Domain Transfer via Semantic Skill Imitation

no code implementations14 Dec 2022 Karl Pertsch, Ruta Desai, Vikash Kumar, Franziska Meier, Joseph J. Lim, Dhruv Batra, Akshara Rai

We propose an approach for semantic imitation, which uses demonstrations from a source domain, e. g. human videos, to accelerate reinforcement learning (RL) in a different target domain, e. g. a robotic manipulator in a simulated kitchen.

Reinforcement Learning (RL) Robot Manipulation

PATO: Policy Assisted TeleOperation for Scalable Robot Data Collection

no code implementations9 Dec 2022 Shivin Dass, Karl Pertsch, Hejia Zhang, Youngwoon Lee, Joseph J. Lim, Stefanos Nikolaidis

Large-scale data is an essential component of machine learning as demonstrated in recent advances in natural language processing and computer vision research.

Skill-based Meta-Reinforcement Learning

no code implementations ICLR 2022 Taewook Nam, Shao-Hua Sun, Karl Pertsch, Sung Ju Hwang, Joseph J Lim

While deep reinforcement learning methods have shown impressive results in robot learning, their sample inefficiency makes the learning of complex, long-horizon behaviors with real robot systems infeasible.

Continuous Control Meta-Learning +3

Task-Induced Representation Learning

no code implementations ICLR 2022 Jun Yamada, Karl Pertsch, Anisha Gunjal, Joseph J. Lim

We investigate the effectiveness of unsupervised and task-induced representation learning approaches on four visually complex environments, from Distracting DMControl to the CARLA driving simulator.

Contrastive Learning Imitation Learning +2

Demonstration-Guided Reinforcement Learning with Learned Skills

no code implementations ICLR Workshop SSL-RL 2021 Karl Pertsch, Youngwoon Lee, Yue Wu, Joseph J. Lim

Prior approaches for demonstration-guided RL treat every new task as an independent learning problem and attempt to follow the provided demonstrations step-by-step, akin to a human trying to imitate a completely unseen behavior by following the demonstrator's exact muscle movements.

reinforcement-learning Reinforcement Learning (RL) +1

Motion Planner Augmented Reinforcement Learning for Robot Manipulation in Obstructed Environments

no code implementations22 Oct 2020 Jun Yamada, Youngwoon Lee, Gautam Salhotra, Karl Pertsch, Max Pflueger, Gaurav S. Sukhatme, Joseph J. Lim, Peter Englert

In contrast, motion planners use explicit models of the agent and environment to plan collision-free paths to faraway goals, but suffer from inaccurate models in tasks that require contacts with the environment.

reinforcement-learning Reinforcement Learning (RL) +1

Accelerating Reinforcement Learning with Learned Skill Priors

2 code implementations22 Oct 2020 Karl Pertsch, Youngwoon Lee, Joseph J. Lim

We validate our approach, SPiRL (Skill-Prior RL), on complex navigation and robotic manipulation tasks and show that learned skill priors are essential for effective skill transfer from rich datasets.

reinforcement-learning Reinforcement Learning (RL)

Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors

1 code implementation NeurIPS 2020 Karl Pertsch, Oleh Rybkin, Frederik Ebert, Chelsea Finn, Dinesh Jayaraman, Sergey Levine

In this work we propose a framework for visual prediction and planning that is able to overcome both of these limitations.

Goal-Conditioned Video Prediction

no code implementations25 Sep 2019 Oleh Rybkin, Karl Pertsch, Frederik Ebert, Dinesh Jayaraman, Chelsea Finn, Sergey Levine

Prior work on video generation largely focuses on prediction models that only observe frames from the beginning of the video.

Imitation Learning Video Generation +1

Keyframing the Future: Discovering Temporal Hierarchy with Keyframe-Inpainter Prediction

no code implementations25 Sep 2019 Karl Pertsch, Oleh Rybkin, Jingyun Yang, Konstantinos G. Derpanis, Kostas Daniilidis, Joseph J. Lim, Andrew Jaegle

To flexibly and efficiently reason about temporal sequences, abstract representations that compactly represent the important information in the sequence are needed.

Temporal Sequences

Learning what you can do before doing anything

no code implementations ICLR 2019 Oleh Rybkin, Karl Pertsch, Konstantinos G. Derpanis, Kostas Daniilidis, Andrew Jaegle

We introduce a loss term that encourages the network to capture the composability of visual sequences and show that it leads to representations that disentangle the structure of actions.

Video Prediction

iPose: Instance-Aware 6D Pose Estimation of Partly Occluded Objects

no code implementations5 Dec 2017 Omid Hosseini Jafari, Siva Karthik Mustikovela, Karl Pertsch, Eric Brachmann, Carsten Rother

We address the task of 6D pose estimation of known rigid objects from single input images in scenarios where the objects are partly occluded.

6D Pose Estimation 6D Pose Estimation using RGB +2

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