7 accepted papers
A deep understanding of kinematic structures is essential for robot motion and interaction with the environment. Such understanding is captured through articulated objects, which are essential for physical simulation, motion planning, and policy learning. However, creating these models, particularly…
AI agents may soon become capable of autonomously completing valuable, long-horizon tasks in diverse domains. Current benchmarks either do not measure real-world tasks, or are not sufficiently difficult to meaningfully measure frontier models. To this end, we present Terminal-Bench 1.5: a carefully…
Finding an high-quality solution for the tabletop object rearrangement planning is a challenging problem. Compared to determining a goal arrangement [1], rearrangement planning is challenging due to the dependencies between objects and the buffer capacity available to hold objects. Although [3] has
We introduce UniGen, a unified multimodal large language model (MLLM) capable of image understanding and generation. We study the full training pipeline of UniGen from a data-centric perspective, including multi-stage pre-training, supervised fine-tuning, and direct preference optimization. More imp…
Current multimodal and multitask foundation models, like 4M or UnifiedIO, show promising results. However, their out-of-the-box abilities to accept diverse inputs and perform diverse tasks are limited by the (usually small) number of modalities and tasks they are trained on. In this paper, we develo…
Numerous classes of robotics motion planning problems involve searching in constrained configuration spaces where the constraints change during different stages of the motion, and these kinds of motion planning problems are named multi-modal problems. The most common method to solve these problems i…
Nowadays, a number of grasping algorithms [1], [2] have been proposed, that can predict a candidate of grasp poses, even for unseen objects. This enables a robotic manipulator to pick-and-place such objects. However, some of the predicted grasp poses to stably lift a target object may not be directl…