In this summary article, organisers of a tutorial and a workshop at IJCAI-ECAI 2026 pick their key takeaways from their respective sessions. This tutorial was a practical, hands-on tutorial on the theory and methods for handling missing data in tabular and imaging settings, from statistical baselines to autoencoders and generative adversarial networks. The missingness mechanism is more important than the choice of imputation method. MCAR, MAR, and MNAR settings call for different treatment, and MNAR, the most challenging mechanism, is the one that most methods do not handle well. Deep generative imputation is not always better.

Russia and Iran built alternative financial channels, nuclear cooperation at Bushehr and energy projects to counter Western sanctions, a nonpublic 2024-2026 roadmap reveals.

People become more suspicious of a humanoid robot that makes errors, especially when the robot is an expressive conversation partner. In our new study published in the journal Science Robotics, we had 50 people hold conversations and make joint decisions with the commercial humanoid robot Pepper, which is designed to be expressive and recognize emotions. Sometimes we had the robot give sound advice. Sometimes we had it make conversational mistakes, interrupting people or pushing illogical suggestions. For some participants, the robot was animated, using gestures, eye contact and nods.

Robots have been a prolific theme in Japanese pop culture and media since the 1950s, which includes global icons like the, and ( 1). Perhaps not coincidentally, Japanese citizens have a positive outlook on robotic technologies and their use in the labor sector compared to many other nations ( 2); much to their advantage, as robotics continues to be a vital pillar for ensuring Japan's economic resilience and future growth. Here, we discuss priorities and long-term agendas as presented in Japan's national robotics strategies. Although weather control is among Japan's lofty research goals for the next 25 years ( 3), geological disturbances remain inevitable. As such, disaster mitigation and response are high priorities that serve as motivation for robotics development in Japan ( 4).

"Who's Ahead, Who's Behind, and Where We Are Headed Next in the U.S.-China Technology Competition?" [5] D. J. Hicks and R. Simmons, "The National Robotics Initiative: A Five-Year Retrospective," in IEEE Robotics & Automation Magazine, vol.

You can read the roadmap in full here . Lucy Smith is Senior Managing Editor for Robohub and AIhub. Lucy Smith is Senior Managing Editor for Robohub and AIhub.

The positive with the lowest rankx1 has a gradient in the good direction, since it leads to increasex1'sscore because the correct ordering is not reached (the negativeinstance WecanseeinFig.2bthatthis change enables tohavegradients inthecorrect directions forthetwopositiveinstancesx1 and x2 (tending to increase their scores), and for the negative instancex3 (tending to decrease its score). However there is still vanishing gradients. Overall, LSupAP has all the desired properties: i) A correct gradient flow during training, ii) No vanishing gradients while the correct ranking isnot reached, iii)Being anupper bound onthe AP lossLAP. We now write that each positive instance that respects the constraint ofLcalibr. A.3 Choiceofδ In the main paper we introduceδ in Eq. (4) to defineH .

In image retrieval, standard evaluation metrics rely on score ranking, e.g.

In image retrieval, standard evaluation metrics rely on score ranking, e.g.

Abstract-- High degree-of-freedom dual-arm robots are becoming increasingly common due to their morphology enabling them to operate effectively in human environments. However, motion planning in real-time within unknown, changing environments remains a challenge for such robots due to the high dimensionality of the configuration space and the complex collision-avoidance constraints that must be obeyed. In this work, we propose a novel way to alleviate the curse of dimensionality by leveraging the structure imposed by shared joints (e.g. First, we build two dynamic roadmaps (DRM) for each kinematic chain (i.e. Then, we show that we can leverage this structure to efficiently search through the composition of the two roadmaps and largely sidestep the curse of dimensionality. Finally, we run several experiments in a real-world grocery store with this motion planner on a 19 DoF mobile manipulation robot executing a grocery fulfillment task, achieving 0.4s average planning times with 99.9% success rate across more than 2000 motion plans.