Robots Need
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Robots Need More than VLA and World Models
arXiv:2606.06556v1 Announce Type: new Abstract: Generalist robot intelligence is often framed as a policy-scaling problem: collect more robot demonstrations, train larger Vision-Language-Action (VLA) models, and expect broader generalisation. In this position paper, we argue that this framing is incomplete. The central bottleneck is not only policy learning, but the absence of mechanisms that convert the world's abundant unstructured behavioural data into grounded robot supervision.
Is Diversity All You Need for Scalable Robotic Manipulation?
Announce Type: replace Abstract: Data scaling has driven remarkable success in foundation models for Natural Language Processing (NLP) and Computer Vision (CV), yet the principles of effective data scaling in robotic manipulation remain insufficiently understood. In this work, we investigate the nuanced role of data diversity in robot learning by examining three critical dimensions-task (what to do), embodiment (which robot to use), and expert (who demonstrates)-challenging the conventional...
CNBC's The China Connection newsletter: Humanoid robots are great, but they need buyers too
Hi, this is Evelyn, writing to you from Beijing. Welcome to the latest edition of The China Connection — a snapshot of what I'm seeing and hearing from local businesses. Humanoids are popping up everywhere, even reshaping a smartphone manufacturer.
Googly-eyed robot gives dementia-stricken husband his freedom back - and his exhausted wife a much-needed break
Googly-eyed robot gives dementia-stricken husband his freedom back - and his exhausted wife a much-needed break The decades-long quest to build home robots that are both helpful and lifelike — spurred on by fictional machines like The Jetsons’ humanoid maid Rosie —- is still mostly a pipe dream, but some developers are getting closer - Bookmark Robbie, a caregiver robot, now rolls into their living room several times a day, offering crucial support to Brenda and Brian Marquis, who faced...
Do We Really Need Immediate Resets? Rethinking Collision Handling for Efficient Robot Navigation
Announce Type: replace Abstract: Should a single collision necessarily terminate an entire navigation episode? In most deep reinforcement learning (DRL) frameworks for robot navigation, this remains the standard practice: every collision immediately triggers a global environment reset and is penalized as a complete task failure. While a collision during deployment naturally indicates task failure, applying the same treatment during training prevents the agent from exploring challenging...
Worth Remembering: Surprise-Gated Robot Episodic Memory
Announce Type: replace Abstract: Robots solving generalist tasks need to be able to ground instructions in their past experience, since humans may refer to notable past events when giving a task (e.g., ``Take me to where the chemical spill happened yesterday''). Since memory limits make storing all past events infeasible, long-term robot memory must be selective, ideally retaining only those episodes with high utility for future tasks. However, future tasks are not typically given a priori...
Meet Argus: The sea-urchin robot with 20 eyes and legs
Most robots are built to look like something. Engineers designing machines to navigate the real world have, for decades, reached for the same reference points: the human skeleton, the dog's four-legged trot, the insect's crawl. These biological templates have produced impressive machines, but they carry an embedded assumption that a robot needs a front, a back, and a preferred direction of travel.
Worth Remembering: Surprise-Gated Robot Episodic Memory
arXiv:2606.03787v3 Announce Type: replace Abstract: Robots solving generalist tasks need to be able to ground instructions in their past experience, since humans may refer to notable past events when giving a task (e.g., ``Take me to where the chemical spill happened yesterday''). Since memory limits make storing all past events infeasible, long-term robot memory must be selective, ideally retaining only those episodes with high utility for future tasks. However, future tasks are not...
Worth Remembering: Surprise-Gated Robot Episodic Memory
arXiv:2606.03787v1 Announce Type: new Abstract: Robots solving generalist tasks need to be able to ground instructions in their past experience, since humans may refer to notable past events when giving a task (e.g., ``Take me to where the chemical spill happened yesterday''). Since memory limits make storing all past events infeasible, long-term robot memory must be selective, ideally retaining only those episodes with high utility for future tasks. However, future tasks are not typically...
RoboCade: Gamifying Robot Data Collection
Announce Type: replace Abstract: Imitation learning from human demonstrations has become a dominant approach for training autonomous robot policies. However, collecting demonstration datasets is costly: it often requires access to robots and needs sustained effort in a tedious, long process. These factors limit the scale of data available for training policies.