Robotic Nanoparticle Synthesis via Solution-based Processes
Authors: Dasharadhan Mahalingam, Michael Gallagher, Nilanjan Chakraborty, Stanislaus S. Wong
Organizations: Department of Mechanical Engineering, Stony Brook University, Stony Brook, NY 11794, USA · Department of Chemistry, Stony Brook University, Stony Brook, NY 11794, USA
We present a screw geometry-based manipulation planning framework for the robotic automation of solution-based synthesis, exemplified through the preparation of gold and magnetite nanoparticles. The synthesis protocols are inherently long-horizon, multi-step tasks, requiring skills such as pick-and-place, pouring, turning a knob, and periodic visual inspection to detect reaction completion. A central challenge is that some skills, notably pouring, transferring containers with solutions, and turning a knob, impose geometric and kinematic constraints on the end-effector motion. To address this, we use a programming by demonstration paradigm where the constraints can be extracted from a single demonstration. This combination of screw-based motion representation and demonstration-driven specification enables domain experts, such as chemists, to readily adapt and reprogram the system for new experimental protocols and laboratory setups without requiring expertise in robotics or motion planning. We extract sequences of constant screws from demonstrations, which compactly encode the motion constraints while remaining coordinate-invariant. This representation enables robust generalization across variations in grasp placement and allows parameterized reuse of a skill learned from a single example. By composing these screw-parameterized primitives according to the synthesis protocol, the robot autonomously generates motion plans that execute the complete experiment over repeated runs. Our results highlight that screw-theoretic planning, combined with programming by demonstration, provides a rigorous and generalizable foundation for long-horizon laboratory automation, thereby enabling fundamental kinematics to have a translational impact on the use of robots in developing scalable solution-based synthesis protocols.
Figures & tables
Figure 1: Robot performing constrained manipulation tasks as part of Magnetite Nano-Particle synthesis experiment.
Figure 2: User provided kinesthetic demonstrations for the manipulation tasks involved in the nanoparticle synthesis.
Figure 3: Automated Gold Nanoparticle Synthesis using Robots: Sequence of tasks involved in the synthesis of Gold Nanoparticles.
Figure 4: Observable differences in gold nanoparticle concentration when the robot was instructed to perform simple lab tasks, such as stirring.
Figure 5: Gold Nanoparticle growth over time.
Figure 6: Comparison of as-synthesized gold nanoparticles, with and without stirring.
Figure 7: Automated Magnetite Nanoparticle Synthesis using Robots: Sequence of tasks involved in the synthesis of Magnetite Nanoparticles.
Figure 8: Characterization of magnetite nanoparticles synthesized by the robot.
Figure 9: XRD pattern of magnetite nanoparticles synthesized by the robot. Major peaks associated with magnetite are labeled.
Figure 10: Magnetite titration curve and corresponding first derivative.
Wet-lab experimentation serves as the gold standard for hypothesis verification in scientific discovery; yet it is inherently labor-intensive, costly, and safety-critical. Embodied agents hold the promise of automating these tedious workflows, but their development is hindered by the scarcity of real-world training data. While simulation offers a scalable alternative for producing demonstrations, current methods primarily target relatively short-horizon tasks with loosely structured interactions, failing to meet the strict procedural constraints and fine-grained manipulation demands of chemical experiments. To bridge this gap, we introduce RoboChemGym, a framework that autonomously generates high-fidelity manipulation demonstrations aligned with real-world experiment protocols, featuring a self-improving task synthesis mechanism to iteratively refine task execution and scene configurations, enabling the reliable generation of expert trajectories for complex, multi-object protocols exceeding 10 interaction steps. Furthermore, we introduce a hierarchical benchmark that systematically assesses performance across varying granularities, spanning from atomic operations to full-cycle experimental workflows. RoboChemGym sets a scalable paradigm for the automated data synthesis and capability evaluation of embodied agents in intricate chemical tasks, serving as a critical stepping stone toward fully intelligent laboratories.
Chenxi Li, Haiyuan Wan, Rui Li +10
Shanghai Artificial Intelligence Laboratory · Zhejiang University · Tsinghua University +7
Learning from demonstration (LfD) enables robots to learn manipulation skills directly from expert demonstrations but remains challenging for contact-rich tasks involving geometric constraints and force interaction. Existing approaches typically require multiple complete demonstrations and do not support reverse skill execution. In this paper, we present a unified one-shot framework for constrained manipulation that learns both forward and reverse execution from a single, possibly unfinished demonstration. Our method decomposes demonstrations into non-contact and contact phases, with non-contact motion encoded with dynamic movement primitives (DMP), and contact motion represented as a sequence of screw motion primitives segmented by our proposed geometry-driven twist-direction segmentation algorithm. During execution, screw primitives are executed sequentially under admittance-guided pose correction and speed regulation, enabling task completion beyond the demonstrated trajectory length as well as reverse skill execution without additional learning data. Experiments on peg insertion, battery insertion, lock opening, and screw driving tasks demonstrate improved success rates and robustness over segmentation and one-shot trajectory learning baselines. Details are available on the project website: https://tuwien-asl.github.io/LfD-Screw/.
Yexin Hu, Haoyi Zheng, Johannes Heidersberger +1
Autonomous Systems Lab, Technische Universität Wien (TU Wien), Austria · Institute of Robotics and Mechatronics, German Aerospace Center (DLR), Germany
In this paper, we study the problem of manipulation skill acquisition for performing construction activities consisting of repetitive tasks (e.g., building a wall or installing ceiling tiles). Our approach involves setting up a simulated construction activity in a Virtual Reality (VR) environment, where the user can provide demonstrations of the object manipulation skills needed to perform the construction activity. We then exploit the screw geometry of motion to approximate the demonstrated motion as a sequence of constant screw motions. For performing the construction activity, we generate the sequence of manipulation task instances and then compute the joint space motion plan corresponding to each instance using Screw Linear Interpolation (ScLERP) and Resolved Motion Rate Control (RMRC). We evaluate our framework by executing two representative construction tasks: constructing brick walls and installing multiple ceiling tiles. Each task is performed using only a single demonstration, a pick-and-place action for the bricks, and a single ceiling tile installation. Our experiments with a 7-DoF robot in both simulation and hardware demonstrate that the approach generalizes robustly to arbitrarily long construction activities that involve repetitive motions and demand precision, even when provided with just one demonstration. For instance, we can construct walls of arbitrary layout and length by leveraging a single demonstration of placing one brick on top of another.
Wangyi Liu, Dasharadhan Mahalingam, Fanru Gao +2
Department of Mechanical Engineering, Stony Brook University, NY, USA. · Department of Civil Engineering, Stony Brook University, NY, USA.