Object placement in robotic tasks is inherently challenging due to the diversity of object geometries and placement configurations. To address this, we propose AnyPlace, a two-stage method trained entirely on synthetic data, capable of predicting a wide range of feasible placement poses for real-world tasks. Our key insight is that by leveraging a Vision-Language Model (VLM) to identify rough placement locations, we focus only on the relevant regions for local placement, which enables us to train the low-level placement-pose-prediction model to capture diverse placements efficiently. For training, we generate a fully synthetic dataset of randomly generated objects in different placement configurations (insertion, stacking, hanging) and train local placement-prediction models. We conduct extensive evaluations in simulation, demonstrating that our method outperforms baselines in terms of success rate, coverage of possible placement modes, and precision. In real-world experiments, we show how our approach directly transfers models trained purely on synthetic data to the real world, where it successfully performs placements in scenarios where other models struggle -- such as with varying object geometries, diverse placement modes, and achieving high precision for fine placement. More at: https://any-place.github.io.

Chemistry experimentation is often resource- and labor-intensive. Despite the many benefits incurred by the integration of advanced and special-purpose lab equipment, many aspects of experimentation are still manually conducted by chemists, for example, polishing an electrode in electrochemistry experiments. Traditional lab automation infrastructure faces challenges when it comes to flexibly adapting to new chemistry experiments. To address this issue, we propose a human-friendly and flexible robotic system, ORGANA, that automates a diverse set of chemistry experiments. It is capable of interacting with chemists in the lab through natural language, using Large Language Models (LLMs). ORGANA keeps scientists informed by providing timely reports that incorporate statistical analyses. Additionally, it actively engages with users when necessary for disambiguation or troubleshooting. ORGANA can reason over user input to derive experiment goals, and plan long sequences of both high-level tasks and low-level robot actions while using feedback from the visual perception of the environment. It also supports scheduling and parallel execution for experiments that require resource allocation and coordination between multiple robots and experiment stations. We show that ORGANA successfully conducts a diverse set of chemistry experiments, including solubility assessment, pH measurement, recrystallization, and electrochemistry experiments. For the latter, we show that ORGANA robustly executes a long-horizon plan, comprising 19 steps executed in parallel, to characterize the electrochemical properties of quinone derivatives, a class of molecules used in rechargeable flow batteries. Our user study indicates that ORGANA significantly improves many aspects of user experience while reducing their physical workload. More details about ORGANA can be found at https://ac-rad.github.io/organa/.

Chemists need to perform many laborious and time-consuming experiments in the lab to discover and understand the properties of new materials. To support and accelerate this process, we propose a robot framework for manipulation that autonomously performs chemistry experiments. Our framework receives high-level abstract descriptions of chemistry experiments, perceives the lab workspace, and autonomously plans multi-step actions and motions. The robot interacts with a wide range of lab equipment and executes the generated plans. A key component of our method is constrained task and motion planning using PDDLStream solvers. Preventing collisions and spillage is done by introducing a constrained motion planner. Our planning framework can conduct different experiments employing implemented actions and lab tools. We demonstrate the utility of our framework on pouring skills for various materials and two fundamental chemical experiments for materials synthesis: solubility and recrystallization.

Transparent object perception is a crucial skill for applications such as robot manipulation in household and laboratory settings. Existing methods utilize RGB-D or stereo inputs to handle a subset of perception tasks including depth and pose estimation. However, transparent object perception remains to be an open problem. In this paper, we forgo the unreliable depth map from RGB-D sensors and extend the stereo based method. Our proposed method, MVTrans, is an end-to-end multi-view architecture with multiple perception capabilities, including depth estimation, segmentation, and pose estimation. Additionally, we establish a novel procedural photo-realistic dataset generation pipeline and create a large-scale transparent object detection dataset, Syn-TODD, which is suitable for training networks with all three modalities, RGB-D, stereo and multi-view RGB. Project Site: https://ac-rad.github.io/MVTrans/