Reinforcement learning, a subset of artificial intelligence (AI), underscores the capability of a robotic system to understand and evaluate its environment, making consequential decisions to optimize its performance or execute predetermined objectives.

Reinforcement learning packs inherent features that combine to create an interactive learning environment. These myriad characteristics facilitate robotic systems to maneuver through dynamic environments:

• Environmental Interaction: RL involves interaction with the environment to learn the optimal paths and make concrete decisions.
• Dynamic Decision-making: Robotics powered by RL uses action consequences to make superior decisions.
• Goal-Oriented Learning: With a reward-oriented approach, RL considers an action profitable if it brings the robotic system closer to its goal.
• Trial and Error: RL robotic systems bank on the trial-and-error principle to learn from past mistakes.
• Self-learning: As opposed to pre-programming, RL encourages machines to learn from experiences aiding autonomous operations.

Implementing reinforcement learning in robotics demands a well-strategized approach. It starts with problem definition followed by the designing of rewarding strategies and then the selection of the appropriate RL algorithm.

Holistic training to handle real-world scenarios comes next, supported by consistent testing, tuning, and validating of the RL model. The success of RL in robotics relies heavily on setting appropriate goals, ensuring safety during the learning process, and meticulous attention to data collection and analysis.

In light of the its potential, reinforcement learning emerges as an instrumental tool in the world of robotics. However, the complexity and depth of implementing RL necessitate not just technical finesse but also strategic planning and execution. Therefore, it's essential to bear in mind the inherent challenges and devise comprehensive strategies to drive successful RL integrations in robotics.

Integrating reinforcement learning into robotics furnishes a host of compelling benefits, including:

• Flexible Adaptation: RL for robotics promotes enhanced adaptability, enabling the machine to learn and grow autonomously from its interactions and experiences.
• Automated Decision-Making: RL can make robotic systems smart enough to provide real-time decisions without human intervention.
• Increased Efficiency: RL helps train robotics to attain optimal efficiency in their functionality by refining their decision-making processes.
• Advanced Problem-Solving: By discreetly breaking down complex problems into simpler sub-tasks, RL enhances the problem-solving abilities of robotic systems.
• Scalability: RL has great scalability potential, ensuring that a single RL model can train numerous robots.

Despite its considerable promises, reinforcement learning for robotics has certain drawbacks:

• Computational Complexity: RL involves high computational complexity. The quest for the optimal policy could demand extensive computational resources.
• Training Time: RL generally requires prolonged training timeframes to generalize the learned tasks.
• Sample Inefficiency: Robots often need numerous examples or 'trials' before converging on an optimal solution, which might be resource-draining.
• Sub-Optimal Solutions: RL may occasionally settle for sub-optimal solutions, especially in complicated tasks necessitating multi-step planning.