This late-breaking report presents RCareGen, a graphical interface that integrates natural language commands with RCare World, a physics simulator for robotic caregiving scenarios. RCareGen has three core modules: (1) a front-end web interface, (2) an LLM-based code generator, and (3) the RCareWorld simulation backend. The front-end web interface enables novice users to input natural language, which is translated into Python code by an LLM-based code generator. This generated code interacts with RCareWorld APIs to run the simulation backend, facilitating scene setup, modifications, simple movements, and human-robot interaction tasks. Additionally, the system supports iterative feedback, allowing users to refine scenes and tasks interactively. By simplifying simulation setup and enhancing task diversity, RCareGen introduces a novel interface that democratizes robot simulation and programming across diverse domains.

Bed-to-wheelchair transferring is a ubiquitous activity of daily living (ADL), but especially challenging for caregiving robots with limited payloads. We develop a novel algorithm that leverages the presence of other assistive devices: a Hoyer sling and a wheelchair for coarse manipulation of heavy loads, alongside a robot arm for fine-grained manipulation of deformable objects (Hoyer sling straps). We instrument the Hoyer sling and wheelchair with actuators and sensors so that they can become intelligent agents in the algorithm. We then focus on one subtask of the transferring ADL-tying Hoyer sling straps to the sling bar-that exemplifies the challenges of transfer: multi-agent planning, deformable object manipulation, and generalization to varying hook shapes, sling materials, and care recipient bodies. To address these challenges, we propose CART-MPC, a novel algorithm based on turn-taking multi-agent model predictive control that uses a learned neural dynamics model for a keypoint-based representation of the deformable Hoyer sling strap, and a novel cost function that leverages linking numbers from knot theory and neural amortization to accelerate inference. We validate it in both RCareWorld [1] simulation and real-world environments. In simulation, CART-MPC successfully generalizes across diverse hook designs, sling materials, and care recipient body shapes. In the real world, we show zero-shot sim-to-real generalization capabilities to tie deformable Hoyer sling straps on a sling bar towards transferring a manikin from a hospital bed to a wheelchair. See our websitefor supplementary materials: https://emprise.cs.cornell.edu/cart-mpc/.