I had a lovely time in Seoul presenting Trust Region Inverse RL (arxiv.org/pdf/2605.11020) at @icmlconf.bsky.social! Also got a chance to visit and give a talk at the Robot Learning Lab at SNU (rllab.snu.ac.kr). They’re doing amazing work on RL for robotics 🦾

The RL4VLA Workshop has come to an end after an incredible Friday at #RSS2026 in Sydney 🇦🇺 We brought together researchers to discuss how RL can advance VLA models, the remaining challenges, and what it will take for robotics to have its own "RL moment" as we've seen with LLMs.

🏆 Tim Faust received the Freudenberg award for his thesis on Model Simplification in Value-based Deep RL!! 🎉 Tim did his thesis at our lab supervised by @theo-vincent.bsky.social This award rewards the best master's thesis across all fields of research TU Darmstadt with 10k€

Congratulations to the whole team for this achievement!!

Théo Vincent@theo-vincent.bsky.social · 2w ago

🏅Gradient Iterated Temporal-Difference Learning received Best Paper Award for empirical RL research @rl-conference.bsky.social🥳

📢 Excited to share that our paper 𝗧𝗿𝘂𝘀𝘁 𝗥𝗲𝗴𝗶𝗼𝗻 𝗜𝗻𝘃𝗲𝗿𝘀𝗲 𝗥𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 (𝗧𝗥𝗜𝗥𝗟) has been accepted at @icmlconf.bsky.social 2026! 🌐 Project page: lnkd.in/dqNi68Mk 📄 Paper: lnkd.in/dzbcCbjU #ICML2026 #ReinforcementLearning #InverseRL #ImitationLearning #Robotics

Why do we keep using semi-gradient methods when they can diverge?🤨 Gradient TD methods are often overlooked, while they have convergence guarantees! @rl-conference.bsky.social, we will present the first gradient TD method shown to be competitive against semi-gradient methods on deep RL benchmarks🏆

Huge thanks to my co-authors Davide Tateo, Christopher Mower, @haithambouammar.bsky.social , @jan-peters.bsky.social , and @olegarenz.bsky.social . And to @ias-tudarmstadt.bsky.social, Noah's Ark Lab, @hessianai.bsky.social , @dfki.bsky.social , and Robotics Institute Germany for their support.

I really liked working on this project with Kevin Gerhardt, @yogesh1q2w.bsky.social, Habib Maraqten, Adam White, Martha White, @jan-peters.bsky.social, and Carlo D'eramo💡

⚡️What if we could design robots with gradients? 🤖 Introducing Shape Your Body: we train one multi-embodiment policy + value function, then optimize new robot designs through value gradients. 🔗 Try out our interactive demo here: nico-bohlinger.github.io/shape-your-b...

Learning five-ball juggling on the second attempt, with two Barrett WAMs. Most humans take years of practice. Paper and videos: kai-ploeger.com/residual-juggling #robotics #juggling

Proud to share our latest work, accepted at @iclr-conf.bsky.social 2026: APPLE! 🍎 TL;DR: APPLE is a novel reinforcement learning framework for solving active perception problems. #ICLR2026 #Robotics #MachineLearning #ActivePerception #RL @ias-tudarmstadt.bsky.social

🧵 Accepted at @iclr-conf.bsky.social! Target networks stabilize bootstrapping in RL 🛡️ But induce slow-moving targets 🐢 Online networks adapt fast ⚡ But can diverge with function approximation 💥 𝗠𝗜𝗡𝗧𝗢 🌿 uses the online network 𝗼𝗻𝗹𝘆 𝗶𝗳 𝗶𝘁 𝗰𝗮𝗻 — yielding faster 𝘢𝘯𝘥 more stable RL. Here’s how 👇

Should we use a target network in deep value-based RL?🤔 The answer has always been YES or NO, as there are pros and cons. @iclr-conf.bsky.social, I will present iS-QN, a method that lies in between this binary view, collecting the pros while reducing the cons🚀

🥳Our paper "Floating-Base Deep Lagrangian Networks (FeLaN)" has been accepted to #ICRA2026. FeLaN: a grey-box approach for physically consistent SysID of floating-base robots (humanoids, quadrupeds). 📄 arxiv.org/abs/2510.17270 💻 Soon! 🌐 schulze18.github.io/felan_website/

I'm super excited to have been named an #NVIDIA Graduate Fellowship Finalist! 💚 Huge thanks to my supervisor @jan-peters.bsky.social and all my collaborators. Can't wait to join the NVIDIA Seattle Robotics Lab for my internship next summer! 🤖 blogs.nvidia.com/blog/graduat...

NVIDIA Awards up to $60,000 Research Fellowships to PhD Students

The Graduate Fellowship Program announced the latest awards of up to $60,000 each to 10 Ph.D. students involved in research that spans all areas of computing innovation.

blogs.nvidia.com

🎉 Really excited, our paper "XQC: Well-conditioned Optimization Accelerates Deep Reinforcement Learning" has been accepted at #ICLR2026. If you are interested in reinforcement learning, sample-efficiency, compute-efficiency go check it out. See you in Rio!

Daniel Palenicek@daniel-palenicek.bsky.social · 11mo ago

🚀 New preprint! Introducing XQC— a simple, well-conditioned actor-critic that achieves SOTA sample efficiency in #RL ✅ ~4.5× fewer parameters than SimbaV2 ✅ Scales to vision-based RL 👉 arxiv.org/pdf/2509.25174 Thanks to Florian Vogt @joemwatson.bsky.social @jan-peters.bsky.social

Just graduated PhD student #45 ... Niklas Wilhelm Funk did an outstanding job defending a doctoral dissertation on Learning Robotic Manipulation through Vision, Touch, and Spatially Grounded Representations! Major insights on many different aspects of manipulation...

Just graduated PhD student #45 ... Niklas Wilhelm Funk did an outstanding job defending a doctoral dissertation on Learning Robotic Manipulation through Vision, Touch, and Spatially Grounded Representations! Major insights on many different aspects of manipulation...

@timschneider94.bsky.social will present "Analysing the Interplay of Vision and Touch for Dexterous Insertion Tasks" by Janis Lenz, Tim Schneider, Theo Gruner, @daniel-palenicek.bsky.social, and @jan-peters.bsky.social 🗓️ 13.06, 16:30 - 19:30 📍 Poster 100 See bsky.app/profile/tims...

@timschneider94.bsky.social · last yr.

Stoked to present another work at RLDM 2025! If you’re into dexterous robotics, multimodal RL, or tactile sensing, swing by Poster 100 today to see what we cooked up 🦾✨ #Robotics #TactileSensing #RL #DexterousManipulation @ias_tudarmstadt 🧵

Our work introduces a geometrically-aware approach that brings motion planning into Bayesian goal inference—an early but promising direction. With @anindex.bsky.social , Theo Gruner, @joemwatson.bsky.social , @georgiachal.bsky.social & @jan-peters.bsky.social

@kay-pompetzki.bsky.social will present "Geometrically-Aware Goal Inference: Leveraging Motion Planning as Inference" by Kay Pompetzki, @anindex.bsky.social, Theo Gruner, @georgiachal.bsky.social, and @jan-peters.bsky.social 🗓️ 13.06, 16:30 - 19:30 📍 Poster 86 See bsky.app/profile/kay-...

Kay Pompetzki@kay-pompetzki.bsky.social · last yr.

Could geometric cues help improve goal inference in robotics? We explore this question at #RLDM today—Spot 86. Stop by if you're curious about bridging motion planning and intent prediction.

🦾 By combining EPVFs with massive parallelism and careful regularization, we close the gap with state-of-the-art DRL in complex environments. 🔗 Full paper: arxiv.org/abs/2502.11949 ✨ Finally, many thanks to @jan-peters.bsky.social and @ias-tudarmstadt.bsky.social for the support!

Massively Scaling Explicit Policy-conditioned Value Functions

We introduce a scaling strategy for Explicit Policy-Conditioned Value Functions (EPVFs) that significantly improves performance on challenging continuous-control tasks. EPVFs learn a value function V(...

arxiv.org

We will also present "Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization" by @daniel-palenicek.bsky.social, Florian Vogt, @joemwatson.bsky.social, and @jan-peters.bsky.social. 🗓️ 13.06, 16:30 - 19:30 📍 Poster 50 See bsky.app/profile/did:...

Daniel Palenicek@daniel-palenicek.bsky.social · last yr.

🚀 New preprint "Scaling Off-Policy Reinforcement Learning with Batch and Weight Normalization"🤖 We propose CrossQ+WN, a simple yet powerful off-policy RL for more sample-efficiency and scalability to higher update-to-data ratios. 🧵 t.co/Z6QrMxZaPY #RL @ias-tudarmstadt.bsky.social

Or come to my talk @ International Symposium on Adaptive Motion of Animals and Machines and LokoAssist Symposium (AMAM) on Friday at TU Darmstadt Thanks to @ias-tudarmstadt.bsky.social, @jan-peters.bsky.social

🎤 Very excited to give a talk @cohereforai.bsky.social next week Friday 🎤 I will be presenting the research I have been working on for the last 2 years with Carlo D'Eramo, @jan-peters.bsky.social, and many more collaborators!

🎉Our paper "Context-Aware Deep Lagrangian Networks for MPC" was accepted at #IROS2025! We present CaDeLaC: adaptive physics-consistent robot control via online SysID + MPC. Paper: arxiv.org/abs/2506.15249 Big thanks to @ias-tudarmstadt.bsky.social @jan-peters.bsky.social @olegarenz.bsky.social

Pushing for #icra but still missing real robot experiments? 😰 Skip the ROS headaches — get your Franka robot running in minutes with franky! 🦾 Super beginner-friendly, Pythonic, and fast to set up. 🔗 github.com/TimSchneider... @ias-tudarmstadt.bsky.social @jan-peters.bsky.social

GitHub - TimSchneider42/franky: High-Level Control Library for Franka Robots with Python and C++ Support

High-Level Control Library for Franka Robots with Python and C++ Support - GitHub - TimSchneider42/franky: High-Level Control Library for Franka Robots with Python and C++ Support

github.com

🚀 New preprint! Introducing XQC— a simple, well-conditioned actor-critic that achieves SOTA sample efficiency in #RL ✅ ~4.5× fewer parameters than SimbaV2 ✅ Scales to vision-based RL 👉 arxiv.org/pdf/2509.25174 Thanks to Florian Vogt @joemwatson.bsky.social @jan-peters.bsky.social

Warming up for #IROS2025 🔥 We’re releasing CaDeLaC as open source! - Training pipeline - Integration with acados for adaptive physics-consistent MPC - Simulation + real Franka torque control 💻 github.com/Schulze18/ca... @ias-tudarmstadt.bsky.social @jan-peters.bsky.social @olegarenz.bsky.social

GitHub - Schulze18/cadelac: Context-Aware Deep Lagrangian Networks for Model Predictive Control (CaDeLaC)

Context-Aware Deep Lagrangian Networks for Model Predictive Control (CaDeLaC) - Schulze18/cadelac

github.com

🇰🇷 Conferences are about finally meeting your collaborators from all around the world! Check out our work on Embodiment Scaling Laws @CoRL2025 We investigate cross-embodiment learning as the next axis of scaling for truly generalist policies 📈 🔗 All details: embodiment-scaling-laws.github.io