PaperFlume
⌕
Accounts · soon

Library · RL for control

0:00

ReWiND: Language-Guided Rewards Teach Robot Policies without New Demonstrations

Jiahui Zhang, Yusen Luo, Abrar Anwar, Sumedh Anand Sontakke, Joseph J Lim, Jesse Thomason, Erdem Bıyık, Jesse Zhang

CoRL 2025 6:35 10 chapters CC BY 4.0 · adaptation, not endorsed by the authors Source checked · visual review incomplete

Download the deck .pptx Transcript .txt · cited Captions .vtt

Editable text and shapes, not pictures of slides.

Feedback

Was this worth your five minutes?

We are in beta. Tell us whether it helped — and if something is wrong, tell us that too.

Not rated
Goes to a person, not a form queue.
Next

Papers near this one.

Walkthroughs

5:32

Multi-critic Learning for Whole-body End-effector Twist Tracking

shares RL for control
5:21

Multi-Loco: Unifying Multi-Embodiment Legged Locomotion via Reinforcement Learning Augmented Diffusion

shares RL for control
10:43

TopoCut: Learning Multi-Step Cutting with Spectral Rewards and Discrete Diffusion Policies

shares RL for control

Short cuts

0:59

Multi-critic Learning for Whole-body End-effector Twist Tracking

shares RL for control
0:58

Multi-Loco: Unifying Multi-Embodiment Legged Locomotion via Reinforcement Learning Augmented Diffusion

shares RL for control
1:21

TopoCut: Learning Multi-Step Cutting with Spectral Rewards and Discrete Diffusion Policies

shares RL for control
Authors

Want one of these for your paper?

Send the PDF and we make the walkthrough, the deck and the reel. It stays private unless you publish it yourself.

Add your paper How it works
01

Something people finishFive minutes of method beats a PDF nobody opens past the abstract.

02

Slides for your next talkAn editable deck built from your own figures, with the narration in the notes.

03

A cut you can postFifty seconds, vertical, for the places a paper link gets ignored.

04

A page that can be foundA cited transcript sits on the page, so the words in your method are searchable.

Based on “ReWiND: Language-Guided Rewards Teach Robot Policies without New Demonstrations” by Jiahui Zhang, Yusen Luo, Abrar Anwar, Sumedh Anand Sontakke, Joseph J Lim, Jesse Thomason, Erdem Bıyık, Jesse Zhang · CoRL 2025 · CC BY 4.0 · licence checked 2026-10-24 · Changed from the original: rewritten as a narrated walkthrough with slides, using the paper's own figures. An adaptation, not endorsed by its authors.

Request takedown — we aim to answer within 72 hours.