Open Access

This thesis presents ReLoop_RLive, a modular framework for vision-based active positioning research focused on the Sim2Real gap. The framework consists of two independent components. The first is a low-cost physical demonstrator built around the Sphero BOLT+ robot, accessed over HTTP. The second is a simulation backend whose physics and rendering components can be swapped independently. Two simulation backends of deliberately different fidelity are implemented, namely a high-fidelity SAPIEN engine with physically based rendering, and a lightweight Simple backend using a linear kinematic model and OpenCV rendering. The visual gap is quantified, where the SAPIEN-based backend achieves a lower score than Simple, confirming that its physically based renderer produces observations statistically closer to real camera images, though it still falls short of perfectly replicating reality. Physical gap analysis reveals significant displacement errors in both backends on short-duration trajectories. While both environments successfully train PPO agents under stationary, in-distribution conditions, and both agents generalize successfully between simulation backends, zero-shot transfer to the physical demonstrator reveals a clear asymmetry. The agent trained in the high-fidelity SAPIEN environment achieves a zero-shot success rate of 0.95 on the real robot, at the cost of a substantially increased mean episode length compared to simulation. The Simple-trained agent, by contrast, achieves only 0.4 on the real demonstrator, below the random-action baseline of 0.45, indicating that the policy provides no effective control on the physical system. Crucially, scaling the task difficulty to the originally intended random goal configuration presents an open problem, failing to converge across evaluated setups. This confirms that while high-fidelity rendering reduces the transfer gap, bridging the remaining visual and physical mismatches for dynamic active positioning remains an active challenge.