The bin packing problem (BPP) is a challenging combinatorial optimization problem with a number of practical applications. This paper focuses on online 3D-BPP, where the packer makes immediate decisions for a loading position as items continually arrive. We propose a novel reinforcement learning algorithm, GREViT, which utilizes a vision transformer to tackle online 3D-BPP for the first time. By introducing the gradual receptive expansion technique, GREViT overcomes the limitations inherent in learning-based methods that only excel in their trained bins. As a result, GREViT surpasses existing BPP algorithms in packing ratio across various bin sizes. The effectiveness of GREViT in real-world scenarios is validated by its successful demonstrations using a real robot for solving 3D-BPP. The attached video demonstrates GREViT undertaking 3D-BPP in both simulated and real-world environments.

The main challenge in vision-and-language navigation (VLN) is how to understand natural-language instructions in an unseen environment. The main limitation of conventional VLN algorithms is that if an action is mistaken, the agent fails to follow the instructions or explores unnecessary regions, leading the agent to an irrecoverable path. To tackle this problem, we propose Meta-Explore, a hierarchical navigation method deploying an exploitation policy to correct misled recent actions. We show that an exploitation policy, which moves the agent toward a well-chosen local goal among unvisited but observable states, outperforms a method which moves the agent to a previously visited state. We also highlight the demand for imagining regretful explorations with semantically meaningful clues. The key to our approach is understanding the object placements around the agent in spectral-domain. Specifically, we present a novel visual representation, called scene object spectrum (SOS), which performs category-wise 2D Fourier transform of detected objects. Combining exploitation policy and SOS features, the agent can correct its path by choosing a promising local goal. We evaluate our method in three VLN benchmarks: R2R, SOON, and REVERIE. Meta-Explore outper-forms other baselines and shows significant generalization performance. In addition, local goal search using the proposed spectral-domain SOS features significantly improves the success rate by 17.1% and SPL by 20. 6% against the state-of-the-art method of the SOON benchmark. Project page: https://rllab-snu.github.io/projects/Meta-Explore/doc.html

This paper emphasizes the importance of a robot's ability to refer to its task history, especially when it exe-cutes a series of pick-and-place manipulations by following language instructions given one by one. The advantage of referring to the manipulation history can be categorized into two folds: (1) the language instructions omitting details but using expressions referring to the past can be interpreted, and (2) the visual information of objects occluded by previous manipulations can be inferred. For this, we introduce a history-dependent manipulation task which objective is to visually ground a series of language instructions for proper pick-and-place manipulations by referring to the past. We also suggest a relevant dataset and model which can be a baseline, and show that our model trained with the proposed dataset can also be applied to the real world based on the CycleGAN. Our dataset and code are publicly available on the project website: https://sites.google.com/view/history-dependent-manipulation.

This paper proposes a muscle-like SMA (Shape Memory Alloy) actuator with an active cooling system for efficient response. An SMA coil spring is embedded into a stretchable coolant vascular for soften structure of robots. In order to design a flexible, lightweight, and fast-response soft actuator with the SMA coil spring and coolant circulation system, a modeling based approach has been conducted. Analysis of coolant effects has been conducted in aspects of heating speed, cooling speed, and energy consumption based on both theoretical and empirical studies. From thermomechanical and heat transfer model between SMA and coolant, the actuation times in the case of heating and cooling phase have been estimated. From experimental results, Mineral oil is selected as the optimal coolant, and the maximum actuation frequency was measured as 0.5Hz for 40% contraction lifting 1kg.