  Reinforcement Learning (RL) has shown remarkable progress in simulation
environments, yet its application to real-world robotic tasks remains limited
due to challenges in exploration and generalisation. To address these issues,
we introduce NAVINACT, a framework that chooses when the robot should use
classical motion planning-based navigation and when it should learn a policy.
To further improve the efficiency in exploration, we use imitation data to
bootstrap the exploration. NAVINACT dynamically switches between two modes of
operation: navigating to a waypoint using classical techniques when away from
the objects and reinforcement learning for fine-grained manipulation control
when about to interact with objects. NAVINACT consists of a multi-head
architecture composed of ModeNet for mode classification, NavNet for waypoint
prediction, and InteractNet for precise manipulation. By combining the
strengths of RL and Imitation Learning (IL), NAVINACT improves sample
efficiency and mitigates distribution shift, ensuring robust task execution. We
evaluate our approach across multiple challenging simulation environments and
real-world tasks, demonstrating superior performance in terms of adaptability,
efficiency, and generalization compared to existing methods. In both simulated
and real-world settings, NAVINACT demonstrates robust performance. In
simulations, NAVINACT surpasses baseline methods by 10-15\% in training success
rates at 30k samples and by 30-40\% during evaluation phases. In real-world
scenarios, it demonstrates a 30-40\% higher success rate on simpler tasks
compared to baselines and uniquely succeeds in complex, two-stage manipulation
tasks.
  Datasets and supplementary materials can be found on our website:
{https://raaslab.org/projects/NAVINACT/}.
