209 citations · 640 across the 25 of their papers we have counts for
35 papers
Solving a Giant Rubik's Cube with a Quadruped Robot
Lukas Amiet, Gabriel Sánchez-Blanco Repgen, Mayank Mittal +4
Pedipulating a freestanding articulated object requires controlling its pose and internal configuration while maintaining balance. We demonstrate this capability by autonomously so…
Continual Learning for 6-DoF Grasp Synthesis via Experience and Demonstrations
Giulio Schiavi, Andrei Cramariuc, Michael Pantic +1
Most current grasp synthesis systems are trained offline and remain fixed during deployment. While this works well when deployment conditions resemble the training data, performanc…
Size Doesn't Matter: Material-State Reinforcement Learning for Excavator Transferable Soil Manipulation
Lennart Werner, Pol Eyschen, Sean Costello +3
Earthmoving tasks such as excavation, backfilling, or embankment construction require deliberate repositioning of deformable soil. For these tasks, human operators use all shovel f…
Learning Agile Perceptive Traversal of Sparse 3D Structures for Humanoids
Efe Ongan, Chong Zhang, Boyang Sun +3
Traversing sparse 3D structures requires humanoid robots to perceive thin, overhanging geometry while executing agile, accurate whole-body motions. We study this problem through mo…
Learning Locomotion on Discrete Terrain via Minimal Proximity Sensing
Jiale Fan, Connor Flynn, Tianao Xu +4
Learning-based control has revolutionized dynamic locomotion, yet navigating unstructured terrain remains limited by a robot's incomplete awareness of imminent ground contact. Whil…
High Precision Hydraulic Excavator Control for Heavy-Duty Grading
Lennart Werner, Pol Eyschen, Sean Costello +2
High-precision heavy-duty grading is a common step in earthworks, traditionally carried out manually by skilled operators. Removing a significant amount of material while achieving…
Learning Multi-Agent Local Collision-Avoidance for Collaborative Carrying tasks with Coupled Quadrupedal Robots
Francesca Bray, Simone Tolomei, Andrei Cramariuc +2
Robotic collaborative carrying could greatly benefit human activities like warehouse and construction site management. However, coordinating the simultaneous motion of multiple rob…
Beyond Cybathlon: On-demand Quadrupedal Assistance for People with Limited Mobility
Carmen Scheidemann, Andrei Cramariuc, Changan Chen +2
Background: Assistance robots have the potential to increase the independence of people who need daily care due to limited mobility or being wheelchair-bound. Current solutions of…
One-Shot Badminton Shuttle Detection for Mobile Robots
Florentin Dipner, William Talbot, Turcan Tuna +2
This paper presents a robust one-shot badminton shuttlecock detection framework for non-stationary robots. To address the lack of egocentric shuttlecock detection datasets, we intr…
DexEvolve: Evolutionary Optimization for Robust and Diverse Dexterous Grasp Synthesis
René Zurbrügg, Andrei Cramariuc, Marco Hutter
Dexterous grasping is fundamental to robotics, yet data-driven grasp prediction heavily relies on large, diverse datasets that are costly to generate and typically limited to a nar…
Pretraining in Actor-Critic Reinforcement Learning for Locomotion
Jiale Fan, Andrei Cramariuc, Tifanny Portela +1
The pretraining-finetuning paradigm has facilitated numerous transformative advancements in artificial intelligence research in recent years. However, in the domain of reinforcemen…
Multi-critic Learning for Whole-body End-effector Twist Tracking
Aravind Elanjimattathil Vijayan, Andrei Cramariuc, Mattia Risiglione +2
Learning whole-body control for locomotion and arm motions in a single policy has challenges, as the two tasks have conflicting goals. For instance, efficient locomotion typically…
GraspQP: Differentiable Optimization of Force Closure for Diverse and Robust Dexterous Grasping
René Zurbrügg, Andrei Cramariuc, Marco Hutter
Dexterous robotic hands enable versatile interactions due to the flexibility and adaptability of multi-fingered designs, allowing for a wide range of task-specific grasp configurat…
CueLearner: Bootstrapping and local policy adaptation from relative feedback
Giulio Schiavi, Andrei Cramariuc, Lionel Ott +1
Human guidance has emerged as a powerful tool for enhancing reinforcement learning (RL). However, conventional forms of guidance such as demonstrations or binary scalar feedback ca…
Learning coordinated badminton skills for legged manipulators
Yuntao Ma, Andrei Cramariuc, Farbod Farshidian +1
Coordinating the motion between lower and upper limbs and aligning limb control with perception are substantial challenges in robotics, particularly in dynamic environments. To thi…
Learning Quiet Walking for a Small Home Robot
Ryo Watanabe, Takahiro Miki, Fan Shi +5
As home robotics gains traction, robots are increasingly integrated into households, offering companionship and assistance. Quadruped robots, particularly those resembling dogs, ha…
Obstacle-Avoidant Leader Following with a Quadruped Robot
Carmen Scheidemann, Lennart Werner, Victor Reijgwart +5
Personal mobile robotic assistants are expected to find wide applications in industry and healthcare. For example, people with limited mobility can benefit from robots helping with…
Whole-body End-Effector Pose Tracking
Tifanny Portela, Andrei Cramariuc, Mayank Mittal +1
Combining manipulation with the mobility of legged robots is essential for a wide range of robotic applications. However, integrating an arm with a mobile base significantly increa…
Cybathlon -- Legged Mobile Assistance for Quadriplegics
Carmen Scheidemann, Andrei Cramariuc, Marco Hutter
Assistance robots are the future for people who need daily care due to limited mobility or being wheelchair-bound. Current solutions of attaching robotic arms to motorized wheelcha…
Path-Constrained State Estimation for Rail Vehicles
Cornelius von Einem, Andrei Cramariuc, Roland Siegwart +2
Globally rising demand for transportation by rail is pushing existing infrastructure to its capacity limits, necessitating the development of accurate, robust, and high-frequency p…
Local and Global Information in Obstacle Detection on Railway Tracks
Matthias Brucker, Andrei Cramariuc, Cornelius von Einem +2
Reliable obstacle detection on railways could help prevent collisions that result in injuries and potentially damage or derail the train. Unfortunately, generic object detectors do…
maplab 2.0 -- A Modular and Multi-Modal Mapping Framework
Andrei Cramariuc, Lukas Bernreiter, Florian Tschopp +5
Integration of multiple sensor modalities and deep learning into Simultaneous Localization And Mapping (SLAM) systems are areas of significant interest in current research. Multi-m…
Descriptellation: Deep Learned Constellation Descriptors
Chunwei Xing, Xinyu Sun, Andrei Cramariuc +5
Current descriptors for global localization often struggle under vast viewpoint or appearance changes. One possible improvement is the addition of topological information on semant…
NeuralBlox: Real-Time Neural Representation Fusion for Robust Volumetric Mapping
Stefan Lionar, Lukas Schmid, Cesar Cadena +2
We present a novel 3D mapping method leveraging the recent progress in neural implicit representation for 3D reconstruction. Most existing state-of-the-art neural implicit represen…